WO2023007593A1 - Information collection method, information collection device, and information sharing method for mobile terminal - Google Patents

Information collection method, information collection device, and information sharing method for mobile terminal Download PDF

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Publication number
WO2023007593A1
WO2023007593A1 PCT/JP2021/027793 JP2021027793W WO2023007593A1 WO 2023007593 A1 WO2023007593 A1 WO 2023007593A1 JP 2021027793 W JP2021027793 W JP 2021027793W WO 2023007593 A1 WO2023007593 A1 WO 2023007593A1
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Prior art keywords
information
patient
pattern
stress
cause
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PCT/JP2021/027793
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French (fr)
Japanese (ja)
Inventor
浩一 新谷
憲 谷
学 市川
智子 後町
修 野中
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オリンパス株式会社
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Priority to PCT/JP2021/027793 priority Critical patent/WO2023007593A1/en
Publication of WO2023007593A1 publication Critical patent/WO2023007593A1/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • the present invention relates to an information collection method, an information collection device, and an information sharing method for mobile terminals that can collect information on lifestyle habits, etc. that have caused symptoms when examining a patient or the like.
  • Patent Document 1 A diagnostic device, a diagnostic management device, and the like for diagnosing whether or not there is a problem have been proposed (see Patent Document 1).
  • sensor data detected from one or more sensors is analyzed to generate user habit data, and the generated habit data and diagnostic data, which is normal person's lifestyle habit data, are combined. to diagnose whether one is in a disease risk group.
  • Patent Document 1 discloses determining the degree of disease risk by obtaining user's habit data and comparing this data with diagnostic data. That is, although Patent Document 1 discloses obtaining habit data, it is possible to efficiently collect patient habit data based on the diagnosis results when a doctor or the like examines the patient, etc., and identify the cause of the disease. There was no mention of what a new lifestyle would pinpoint.
  • the present invention has been made in view of such circumstances, and provides an information collection method, an information collection device, and information sharing of a mobile terminal that enable efficient selection of evidence data based on the diagnosis results of a doctor or the like.
  • the purpose is to provide a method.
  • an information gathering method acquires a first biometric information pattern in a specific period going back in time and a second biometric information pattern in a specific period going back in time. and a stress determination step of comparing the first biometric information pattern and the second biometric information pattern and determining the stress of the patient based on the comparison.
  • An information collecting method is the information collecting method according to the first invention, in which the first biological information pattern and the second biological information pattern are recorded in association with the current medical condition information of the patient.
  • a recording step is further provided.
  • An information gathering method is the information collection method according to the first invention, wherein prior to obtaining the first and second biometric information patterns, the first and second information is sent to the mobile terminal of the patient. It comprises a communication step of notifying that the biometric information pattern is to be acquired.
  • An information gathering method is the first invention, wherein the cause evidence determination performed by comparing the representative values of the first and second biological information patterns in the specific period is a candidate. A difference between the first and second biometric information patterns in the retroactive period is examined for each cause.
  • An information gathering method is the fourth aspect of the invention, wherein in the causal evidence determination, the retroactive time is set to a time when differences in candidate causes are investigated and the differences are clear.
  • An information gathering method in the first aspect of the invention, selects the types of the first and second biological information patterns based on the candidate cause selected according to the doctor's interview or diagnosis. , to examine the difference for the first and second bioinformation patterns of this selected type.
  • An information gathering method is the information gathering method according to the first aspect of the invention, in which a treatment method for the patient is determined based on the determination result of the stress.
  • An information gathering method is the information gathering method according to the first invention, wherein the first and second biological information patterns compare the interval between the R-wave maximum values of the electrocardiogram with the one obtained immediately before. possible pattern.
  • An information collecting method is the information gathering method according to the first aspect, wherein the first and second biological information patterns are patterns in which the peak values and the number of blood pressure changes in a day can be compared.
  • An information gathering method is the information collection method according to the first invention, wherein the determination of the stress compares fluctuations of the first and second biometric information patterns.
  • An information collecting apparatus includes an acquisition unit that acquires a first biological information pattern in a specific period that goes back a first time, and a second biological information pattern that goes back in a second time in a specific period, and a stress determination unit that compares the first biometric information pattern and the second biometric information pattern and determines the patient's stress based on this comparison;
  • An information gathering method includes a request step of searching for a candidate causing a disease according to an interview or diagnosis by a doctor and requesting the patient's portable terminal to send evidence data for verifying the candidate cause. and, when the evidence data is received from the portable terminal, a comparison step of comparing a first pattern in a specific period of time preceding a first time period and a second pattern of the evidence data in a specific period of time preceding a second time period. and a determination step of determining the cause of the patient's disease from among the cause candidates based on the result of comparing the first pattern and the second pattern.
  • An information gathering method is the twelfth aspect of the invention, wherein the evidence data is biological information data and/or lifestyle information data of the patient.
  • a fourteenth aspect of the present invention is an information gathering method according to the twelfth aspect, wherein the candidate cause includes at least one of stress, lack of exercise, irregular life, intake, and climate.
  • An information gathering method according to a fifteenth invention, in the twelfth invention, has a prescription step of prescribing to the patient based on the determined cause, and transmitting the prescription to the patient to the portable terminal. do.
  • An information collection device includes a retrieval unit that retrieves a candidate for the cause of a disease according to an interview or diagnosis by a doctor, and requests a patient's portable terminal to transmit evidence data for verifying the candidate for the cause. and a communication unit that receives the evidence data from the portable terminal, and compares a first pattern in a specific period going back a first time period and a second pattern in a specific period going back a second time period for the evidence data. and a comparison unit for determining the cause of the patient's disease from among the candidate causes based on the comparison result of the first pattern and the second pattern, the communication unit determining the cause of the patient's disease to the portable terminal.
  • An information sharing method for a mobile terminal comprises a receiving step in which the mobile terminal receives a request signal; and a display control step of displaying the retrieved information on the display unit of the mobile terminal and displaying a switch indicating whether or not to permit transmission of the displayed information to the requester of the displayed information.
  • An information sharing method for a mobile terminal is the method according to the seventeenth aspect, wherein transmission candidate information is displayed in which transmission is permitted and information is not permitted to be selected prior to operation of the switch display. It further has a candidate display step.
  • the information sharing method for a mobile terminal in the seventeenth invention, at least the first The information includes a first biological information pattern in a specific time period that goes back in time and a second biological information pattern in a second specific period that goes back in time.
  • an information sharing method for a mobile terminal wherein the requested information to be retrieved from the information recorded in the recording unit of the mobile terminal is a biometric information pattern. It is information based on time change.
  • a method for sharing information on a mobile terminal according to a twenty-first invention is the method according to the seventeenth invention, further comprising: a stress determination step of determining stress from the biometric information pattern recorded in the recording unit of the mobile terminal; The requested information to be retrieved from the information recorded in the recording unit is stress information.
  • an information collection method it is possible to provide an information collection method, an information collection device, and an information sharing method for mobile terminals that enable efficient selection of evidence data based on the diagnosis results of doctors and the like.
  • FIG. 1 is a block diagram showing the configuration of an information collection system according to one embodiment of the present invention
  • FIG. It is a figure which shows a mode that a doctor and a patient are using the information collection system which concerns on one Embodiment of this invention. It is a figure which shows the display screen in the portable terminal of the information collection system which concerns on one Embodiment of this invention.
  • 4 is a graph showing changes in biological signals such as pulse waves collected in the information collection system according to one embodiment of the present invention. 4 is a graph showing a power spectrum obtained by processing collected pulse waves and the like in the information collection system according to one embodiment of the present invention.
  • 5 is a graph showing the relationship between changes in blood pressure and stress in the information collection system according to one embodiment of the present invention.
  • FIG. 4 is a flow chart showing the operation of the mobile terminal in the information collection system according to one embodiment of the present invention.
  • 4 is a flow chart showing the operation of the server in the information collection system according to one embodiment of the present invention;
  • 4 is a chart showing the relationship between symptoms and evidence data of the symptoms in the information collection system according to one embodiment of the present invention.
  • FIG. 2 is a diagram showing a state in which a doctor or the like is examining lifestyle habits as a cause of symptoms using the information collection system according to one embodiment of the present invention; It is a figure which shows the display screen in the portable terminal of the information collection system which concerns on one Embodiment of this invention.
  • FIG. 4 is a flow chart showing the operation of the server in the information collection system according to one embodiment of the present invention for “evidence determination by comparison of representative values in a specific period”.
  • FIG. 4 is a block diagram illustrating generation of an inference model for inferring advice, treatment methods, etc. in the information collection system according to one embodiment of the present invention.
  • FIG. 2 is a diagram showing the relationship between risk factors for stomach/esophageal diseases and stress and lifestyle habits in the information collection system according to one embodiment of the present invention.
  • mobile terminals such as smartphones have been used independently or in cooperation with smart watches to provide various information of the user of the mobile terminal, such as biological information such as heart rate, blood pressure, and body temperature, as well as sleep/wake-up information. It has become possible to collect time (sleep hours), meal contents, a list of purchased items, lifestyle information such as exercise, and the like.
  • biometric information and lifestyle information recorded in the mobile terminal are acquired, and patterns in a specific period in which symptoms do not occur are compared with patterns in a specific period in which symptoms occur.
  • the cause of the disease is identified based on the biological information and lifestyle information that caused the disease.
  • the doctor's medical examination (interview) results for the patient are obtained (see, for example, S23 and S25 in FIG. 6B), and based on the medical examination (interview) results, related life Habits and biometric information are determined, and in order to obtain information related to the determined lifestyle habits and biometric information, necessary information is selected and requested from information recorded in the patient's mobile terminal.
  • the information acquired from the portable terminal includes the patient's number of steps (acceleration), changes in heart rate over time, and photographs of food. This information may be trended against other people's averages to determine the patient's disease.
  • FIG. 1 is a block diagram showing the configuration of an information collection system according to one embodiment of the present invention.
  • This system has a server 10 , a mobile terminal 20 and a server 30 .
  • the mobile terminal 20 is a terminal such as a smartphone that a user carries and uses in his/her daily life, and has a communication function.
  • the mobile terminal 20 has a control section 21 , a communication section 22 , an input section 23 , a display section 24 , a clock section 25 , a biological information acquisition section 26 , a living information acquisition section 27 , an advice section 28 and a recording section 29 .
  • the control unit 21 controls the mobile terminal 20 as a whole.
  • the control unit 21 is composed of one or a plurality of processors having a processing device such as a CPU and a memory storing a program, and can control each unit in the mobile terminal 20 by executing the program.
  • the communication unit 22 has a communication circuit, has a call function with a general telephone through a mobile phone company, and can be connected to a server or the like through an Internet communication network.
  • biometric information acquired by the biometric information acquiring unit 27 and living information acquired by the living information acquiring unit 27 can be transmitted to the server 10 through the communication unit 22 in response to a request from the server 10.
  • the communication unit 22 can receive biological information comparison results, stress determination results, and lifestyle habit determination results from the server 10 .
  • the input unit 23 is an input interface having a touch sensor on the display panel, other operation members (for example, operation buttons, etc.), an input circuit, and the like. The user can input various information and the like to the mobile terminal 20 through the input unit 23 .
  • the input unit 23 also has an imaging unit and can input an image.
  • the display unit 24 has a display panel, and can display, for example, menu screens, various information, input images, and the like.
  • the clock unit 25 has a calendar function and can measure the date, time, and the like.
  • the biometric information acquisition unit 26 acquires biometric information of the user holding the mobile terminal 20 .
  • the biometric information acquisition unit 26 may acquire biometric information in cooperation with a wearable device such as a smartwatch. This cooperation may be appropriately performed by wired and/or wireless communication. Examples of biological information include blood pressure, pulse, body temperature, and the like.
  • biometric information such as urine can be obtained by linking with a device with a urine sensor such as a smart toilet.
  • the mobile terminal 20 may cooperate with health measuring instruments such as a weight scale, blood pressure gauge, thermometer, blood glucose meter, and pedometer to obtain measured values.
  • a communication unit such as wireless LAN and/or Bluetooth (registered trademark) (bluetooth) and/or a communication control unit may be provided in the biological information acquisition unit 26, or the communication unit 22 You may realize the same function in cooperation with .
  • the life information acquisition unit 27 acquires the life information of the user holding the mobile terminal 20 . If the mobile terminal 20 has a satellite positioning system such as GPS, it can use this to acquire the user's location information. Also, if the mobile terminal 20 has a motion sensor such as a gyro sensor or an acceleration sensor, it can be used to acquire user motion information. If the position and movement of the user are known, it is possible to obtain various life information such as whether the user is exercising such as jogging, sleeping, or working in the office. In addition, with the spread of electronic money, mobile terminals record payment results and the like for each store. The records of purchase details and the like serve as living information, and it becomes possible to determine the user's life patterns and the like based on the place and time of payment.
  • a satellite positioning system such as GPS
  • a motion sensor such as a gyro sensor or an acceleration sensor
  • the lifestyle information acquisition unit 27 can communicate with smart houses, smart home appliances, and the like to acquire lifestyle habit information of the user.
  • the user using the mobile terminal 20 may purchase goods and services through a communication network such as the Internet, and this information is stored in the server 30 or the like.
  • the user using the portable terminal 20 may upload information (including images) indicating daily activities to the external server 30 or the like via SNS or the like. Therefore, the life information acquiring unit 27 may acquire life information stored outside from the server 30 or the like.
  • the advice unit 28 generates various health advices for the user based on various information transmitted from the server 10. This generated advice is displayed on the display unit 24 .
  • the measurement data of wearable health management terminals, weighing scales with communication functions, and body fat scales can be managed on smartphones (mobile terminals), and measurement data can be stored on dedicated websites and apps ( health check-up apps, life improvement apps, etc.) or services that collaborate with them to receive health advice.
  • the mobile terminal 20 may not only use the information in the terminal, but may also cooperate with such services.
  • the advice section 28 may have the functions described above, or may display results obtained in cooperation with an external service.
  • the recording unit 29 has an electrically rewritable non-volatile memory, and records information acquired by the biometric information acquisition unit 26, life information acquisition unit 27, and the like. Also, if there is information such as an image acquired by the input unit 23, it may be recorded.
  • the recording unit 29 functions as a recording unit that associates and records the first biological information pattern and the second biological information pattern with respect to the patient's current medical condition information.
  • the server 10 is a computer or software that has the role of receiving requests and instructions from other computers (clients) and returning information and processing results among the computers on the network.
  • the server 10 can transmit and receive data and the like to and from the portable terminal 20 through the communication unit 12 and the communication unit 22 with the portable terminal 20 and the like. It is assumed that this server 10 cooperates with a computer in a medical facility such as a hospital to diagnose, treat, and manage the health of the user.
  • Portable terminals of doctors and the like and PCs personal computers
  • the server 10 may be arranged outside the medical facility or the like, and may be communicably connected to devices inside the medical facility or the like.
  • the server 10 includes a control unit 11, a communication unit 12, an inquiry input unit 13, a DB search unit 14, a clock unit 15, a biological information comparison unit 16, a stress determination unit 17, a lifestyle determination unit 18, and a recording/DB unit 19. have.
  • the control unit 11 controls the server 10 as a whole.
  • the control unit 11 is composed of one or a plurality of processors having a processing device such as a CPU and a memory storing a program, and can control each unit in the server 10 by executing the program.
  • the communication unit 12 has a communication circuit and can be connected to the mobile terminal 20 and other servers through a network such as the Internet.
  • the mobile terminal 20 is requested to transmit the biometric information acquired by the biometric information acquisition unit 27 in the mobile terminal 20 and the life information acquired by the life information acquisition unit 27, and this information is sent to the communication unit 12.
  • the communication unit 12 can transmit the biological information comparison result, the stress determination result, and the lifestyle determination result performed in the server 10 to the portable terminal 20 .
  • the communication unit 12 functions as an acquisition unit capable of acquiring a first biological information pattern in a specific period that goes back a first time and a second biological information pattern in a specific period that goes back a second time (for example, S29 reference). Prior to acquiring the first and second biological information patterns, the communication unit 12 functions as a communication unit that notifies the portable terminal of the patient that the first and second biological information patterns are to be acquired ( For example, see S29 in FIG. 6B). The communication unit 12 functions as a communication unit that requests the patient's portable terminal to transmit evidence data for verifying the cause candidate (for example, see S29 in FIG. 6B). When the cause of the patient's disease is determined from the cause candidates based on the result of comparing the first pattern and the second pattern, the communication unit transmits a prescription for the patient to the mobile terminal (for example, See S35 in FIG. 6B).
  • a first biological information pattern in a specific period going back a first time and a second biological information pattern in a specific period going back a second time can be obtained, based on the difference between the biological information patterns obtained in these going back times , there has been a change in lifestyle information and/or biometric information, making it easier to determine.
  • the retroactive time is too short, such as when regular health checkups are held every other year, there are cases where differences that can be consciously pointed out may not be apparent. However, after two or three years have passed, there are cases where the data at that time no longer remains.
  • the retroactive time is one year, seasonal factors can be included in the comparison, so it makes sense to compare the information from one year ago.
  • the comparison of the first and second biometric information patterns may be performed together with the health checkup data.
  • the period of retroactive time is too long, the influence of aging and the environment such as work, family structure, and residence may change. In some cases, accurate comparison of sensitive data such as stress becomes difficult. Ideally, the data before the cause of the illness should be compared with the data when some cause has occurred. In addition, the period from the occurrence of the cause to the onset of the disease varies depending on the disease, the magnitude of the stress causing it, the pattern of occurrence, and the stress tolerance of the patient. Have difficulty. However, it is possible to analyze these factors and postulate a suitable time.
  • the difference in retroactive time may be the time difference at which the difference in stress appears most. It may also be a data group containing these periods as a system for selection later. Also, it may be possible to specify an appropriate timing based on the doctor's experience and knowledge. For inexperienced physicians, pre-determined staggers or the like may be used.
  • the inquiry input unit 13 inputs information when a doctor or the like interviews a patient (user) (for example, see S23 in FIG. 6B).
  • text data may be input manually, voice data may be recorded, or voice data may be automatically converted into text data and input.
  • the DB search unit 14 can search databases, etc. recorded in the record/DB 19.
  • the DB search unit 14 functions as a search unit that searches for a candidate that causes a disease according to an inquiry or diagnosis by a doctor (see, for example, S27 in FIG. 6B, S41 in FIG. 10, etc.).
  • the clock unit 15 has a calendar function and can measure the date and time. Normally, the date and time are synchronized through a server for time adjustment, so the date and time information of the clock units of the mobile terminal 20 and the server 10 are the same. The same applies to the server 30, which will be described later.
  • the biometric information comparison unit 16 compares the biometric information acquired by the biometric information acquisition unit 26 of the mobile terminal 20 .
  • the user's biometric information is retroactively recorded in the recording unit 29, and the biometric information comparison unit 16 compares the biometric information of a certain period of time in the past with that of a certain period of time around the present. By comparing, it is possible to know when the user's biometric information has changed. As will be described later, a doctor or the like can identify the cause of a symptom (disease) by comparing past and present biological information, etc., at the time of examination, and prescribe to eliminate the cause.
  • the biometric information comparison unit 16 performs cause evidence determination by comparing the representative values of the first and second biometric information patterns in the specific period, and determines the cause evidence based on the first and the first in the retroactive period for each candidate cause. It functions as a comparison unit that examines the difference between the two biometric information patterns. In the causal evidence determination, the comparison unit examines the differences in the candidate causes as the retroactive time, and determines the time when the differences are clear. Further, the comparison unit selects the types of the first and second biological information patterns based on the candidate cause selected according to the doctor's inquiry or diagnosis, and selects the selected types of the first and second biological information patterns. (See, for example, S27 in FIG. 5B, S41 in FIG. 10, etc.).
  • the biological information comparison unit 16 compares the first pattern in the specific period going back in time with the second pattern in the specific period going back in time for the second time. (See, for example, S31 in FIG. 6B, graphs Gr5 and Gr6 in FIG. 8, etc.).
  • the stress determination unit 17 determines whether or not the user is feeling stress based on biological information and the like. When the user feels stress, changes appear in blood pressure (pulse), sleep time, etc. Therefore, the stress determination unit 17 performs determination based on these changes in biological information. The details of the user's stress will be described later with reference to FIGS. 4A to 5.
  • FIG. The stress determination unit 17 compares the first biological information pattern and the second biological information pattern, and functions as a stress determination unit that determines the patient's stress based on this comparison (see S31 in FIG. 6B, for example). .
  • the lifestyle determination unit 18 determines lifestyle habits based on the user's lifestyle information acquired by the lifestyle information acquisition unit 26 of the mobile terminal 20 .
  • lifestyle habits cause diseases, and conversely, when a person suffers from a disease, there may be lifestyle habits that cause the disease. Therefore, in the present embodiment, the lifestyle determination unit 18 determines what kind of lifestyle the user has based on the lifestyle information acquired by the lifestyle information acquisition unit 26 of the mobile terminal 20, Based on habits, an inference model is generated for giving advice for the user to live a healthy life (see, for example, S3 and S5 in FIG. 6A, S35 in FIG. 6B, and FIG. 11).
  • the recording/DB unit 19 is an electrically rewritable non-volatile memory, and records various information acquired by the server 10 .
  • the recording/database unit 19 records a medical examination recording unit 19b for each profile, biometric information 19c for each profile, and disease-specific causes 19d.
  • the DB search unit 14 described above can search information recorded in the recording/DB unit 19 .
  • the medical examination recording section 19b is a record of medical charts and the like when each patient is examined. In this medical examination recording unit 19b, for example, the patient's basic information and information at the time of medical interview (including electronic medical record information, etc.) are recorded. is recorded.
  • the biometric information acquired by the biometric information acquisition unit 26 of the mobile terminal 20 is recorded in the biometric information 19c for each profile. Lifestyle information and the like acquired by the lifestyle information acquisition unit 27 may be recorded. Further, in the cause by disease 19d, as shown in FIG. 7, symptoms, candidate causes thereof, evidence data supporting the causes, and devices for obtaining the evidence data are recorded.
  • the server 30 is a computer or software that has the role of receiving requests and instructions from other computers (clients) on the network and returning information and processing results.
  • the above-described server 10 was assumed to cooperate with computers in medical facilities such as hospitals to perform diagnosis, treatment, health management, etc. of users, but this server 30 is a general multi-purpose server. be.
  • the communication unit in the server 30 can communicate with the mobile terminal 20 and the server 10, and can also communicate with other mobile terminals and servers. Therefore, general information is recorded in this server 30 .
  • the data that may have caused the disease are explained using the stress value (for example, see the stress determination unit 17. Further details will be given with reference to FIGS. 2 to 5).
  • the data that may have caused the disease are not limited to the stress value, but may include disordered lifestyles and unhealthy lifestyles, including lack of diet, sleep, and exercise. Binge eating and drinking, excessive calorie intake, etc. may also be assumed.
  • the data that may have caused these diseases cannot be determined even by using the judgment results from the health checkup application, the life improvement application, and the request for adding such functions to the application-server cooperation service.
  • the determination may be made in cooperation with the biological information comparing section 16, or the advice section 28 may make the determination.
  • this health checkup application, life improvement application, and application-server cooperation service include a request acquisition step for receiving a request from a doctor, and a patient's health-related information (here, biometric information only) in response to a request from a doctor.
  • This embodiment includes an information sharing method comprising these steps.
  • the terminal may compare the first biometric information pattern and the second biometric information pattern, create and transmit information based on this comparison, and further determine the patient's stress analyzed therefrom. It may have a determination step and transmit the determination result.
  • FIG. 2 shows a doctor 53 examining a patient 51 .
  • the doctor 53 asks the patient 51 various questions, that is, interviews the patient 51, and the patient 51 answers the doctor's 53 questions.
  • the patient 51 does not seem to be particularly aware of stress, but the doctor 53 asks what is the cause of the current symptoms based on the transmitted life information data (biological information). ) to find out what is causing it.
  • the cause it is proposed to think together with the patient 51 what should be improved in order to solve this cause.
  • the user (patient) portable terminal 20 pre-records the user's life information and biological information, and the life information and the like in recent and past specific periods are recorded by the doctor when the doctor examines the patient. is transmitted to the doctor's PC terminal and its server.
  • the server 10 can know the lifestyle habits and the like that caused the symptoms by comparing the lifestyle information and the like in recent and past specific periods. This specific period may be appropriately determined in consideration of the possible cause of the disease, the patient's constitution, and the like. Also, it may be a period in which various periods are tried and the characteristics become clear. 2 and 3, only lifestyle habits are mentioned, but a doctor or the like may also obtain biological information from the patient and examine the cause of the disease.
  • the balance of the autonomic nervous system shifts in favor of the sympathetic nervous system, promoting degeneration of the cardiovascular system, increasing sympathetic tone and decreasing parasympathetic tone, leading to death from heart failure, coronary artery disease, and acute myocardial infarction. It is said to affect various parts of the human body in addition to being related to the rate of injury.
  • the autonomic nervous system is a system that controls blood circulation, breathing, temperature regulation, etc. without conscious intervention. It works in the direction of increasing ability, and the "parasympathetic nervous system" works in the direction of calming the mind and body, suppressing consumption of energy, and storing energy.
  • heart rate variability affects both the sympathetic and parasympathetic nerves
  • the two regions of LF (Low Frequency) and HF (High Frequency) LF value is By examining the balance between 0.05 Hz to 0.15 Hz and 0.15 Hz to 0.40 Hz for HF values, the person's stress can be quantified. That is, the HF component is said to be influenced by parasympathetic nerve activity caused by respiration, and the LF component is said to be influenced by sympathetic and parasympathetic nerve activity. This heart rate variability analysis method will be described later with reference to FIGS. 4A and 4B.
  • the method using a reflective pulse wave sensor irradiates the living body with infrared light, red light, and light with a green wavelength around 550 nm, and uses a photodiode or phototransistor to detect in vivo It is used in many wearable devices because it only measures the reflected light. Oxygenated hemoglobin is present in arterial blood and has the property of absorbing incident light. Therefore, pulse wave signals can be obtained by sensing changes in blood flow (volume changes in blood vessels) that accompany the pulsation of the heart in chronological order. can be measured. It is known that blood flow sensing can also be determined by a transmissive pulse wave sensor or detection of changes in color appearing on the face.
  • the heart has a part called the sinoatrial node, which generates electrical pulses to contract the heart muscle and cause it to beat periodically.
  • the sinoatrial node which generates electrical pulses to contract the heart muscle and cause it to beat periodically.
  • FIGS. 4A and 4B The above explanation of heart rate variability will be explained using FIGS. 4A and 4B.
  • the peak (R wave) of the heartbeat waveform is detected and the time from the peak to the peak of the heartbeat (RRI: RR interval) is measured.
  • RRI RR interval
  • RRI RR interval
  • a stress index LF/HF is calculated. This is calculated by summing the amplitude values in the frequency range shown in the previous explanation of the LF value and HF value.
  • a pulse wave is acquired as shown in the graph Gr1 of FIG. 4A, and the acquired pulse wave is differentiated to acquire a velocity pulse wave as shown in the graph Gr2 of FIG. 4A, By further differentiating this velocity pulse wave, an acceleration pulse wave as shown in graph Gr3 in FIG. 4A is obtained.
  • This accelerated pulse wave is a waveform equivalent to an electrocardiogram and can be analyzed for RRI. If necessary, the waveform is adjusted by filtering, noise removal, etc.
  • the highest peak is called the R wave, and RRI is the interval between the R wave and the next R wave.
  • the accelerated pulse wave shown in graph Gr3 of FIG. 4A corresponds to an electrocardiogram.
  • the peak of the waveform is detected, the time from peak to peak (RRI) is measured, and resampling is performed by linear interpolation.
  • a power spectrum as shown in FIG. 4B can be obtained by Fourier transforming the acceleration pulse wave signal of graph Gr3 in FIG. 4A. In this power spectrum, the stress index LF/HF can be calculated.
  • the LF region is a region of frequencies lower than the specific frequency f_th.
  • the HF region is a frequency region higher than the specific frequency f_th.
  • the numerical value of the HF component may be used as the degree of activity (tone) of the parasympathetic nerve.
  • the LF component appears whether the sympathetic nerve is dominant or the parasympathetic nerve is dominant, the ratio of LF to HF is taken, and LF/HF is used as a stress index (sympathetic nerve activity).
  • FIG. 5 is a graph showing an example of changes in blood pressure in one day. As can be seen from this graph, peaks are seen during meals and bathing, and peaks are also seen when there is stress. It is possible to measure stress using the peak number of blood pressure in a day and its maximum value.
  • Blood pressure can be measured by the "oscillometric method", which involves wrapping an inflatable cuff around the arm.
  • the circulatory system of the whole body is represented mathematically, and the blood flow is modeled using fluid dynamics, with the heart as a pump, blood vessels as elastic containers, and thin blood vessels that connect blood vessels as resistance. may be measured by "hemodynamic sensing”. Blood pressure can be calculated from heart rate using this hemodynamic sensing model.
  • a method of calculating blood pressure by combining heart rate measurement adopted by a smart watch and blood flow measurement by an optical sensor is also known.
  • control unit 21 in mobile terminal 20 controlling each unit in mobile terminal 20 according to a program stored in the memory.
  • the mobile terminal 20 also serves as a device that collects and provides externally information that aids doctors in diagnosing or serves as the source of teacher data for creating an inference model for disease prediction.
  • the application software built into the terminal performs the following functions in cooperation with the wearable terminal and Internet service with which this terminal cooperates.
  • the health diagnosis application, life improvement application, and application-server cooperation service that are recorded in the recording unit of this terminal and that function on the terminal have a request acquisition step for receiving a request from a doctor, and a request acquisition step for receiving a request from a doctor.
  • a first biological information pattern in a specific period going back at least a first time and a second biological information pattern going back a second time from information recorded in a terminal or a device linked thereto Selecting information and transmitting collected patient health-related information (here, it may be assumed to include not only biometric information but also any processing or interpretation thereof) to the physician.
  • the terminal may compare the first biometric information pattern and the second biometric information pattern, create and transmit information based on this comparison, and further determine the patient's stress analyzed therefrom. It may have a determination step and transmit the determination result.
  • FIG. 6A When the operation of the mobile terminal starts, first, questionnaires and vital information are acquired (S1). When the mobile terminal starts operating, the user can generally perform telephone calls, e-mails, and search for information on the Internet by touch operations and switch operations. In addition, when the user carries the mobile terminal, the user's walking is detected by a built-in vibration sensor or the like, and the user's behavior is recorded by the information of the built-in GPS or mobile phone base station. In addition, the built-in camera makes it possible to determine the user's facial expression and facial color, and the captured image helps to obtain biometric information.
  • the body temperature can be detected because the wristwatch-type terminal is in contact with the skin.
  • the element emits light, receives reflected light, and the heart rate, blood pressure, etc. can be determined from the reflected change pattern. Vital information is acquired by acquiring these pieces of information.
  • profiles and questionnaires are in the form of questionnaires, which can be made possible by display and input determination.
  • step S1 when the control unit 21 displays a questionnaire asking the user of the mobile terminal 20 about their health condition on the display unit 24, the user can answer the questionnaire by operating the input unit 23.
  • the contents of the questionnaire include, for example, whether the subject is in good health or not, whether he or she has been feeling stressed recently, and whether he or she is getting good sleep.
  • the biometric information acquisition unit 26 acquires the biometric information of the user. Examples of biological information include blood pressure, pulse, body temperature, and the like. Answers to questionnaires and acquisition results of biometric information are recorded in the recording unit 29 together with date and time information output by the clock unit 25 .
  • This step S1 functions as a recording step for recording the first biological information pattern and the second biological information pattern in association with the patient's current medical condition information.
  • the cause of the symptom is identified by comparing the biometric information patterns in the period when the symptom is present and the period when the symptom is not present (see graphs Gr5 and Gr6 in FIG. 8, for example). ).
  • the biometric information acquisition unit 26 records the biometric information of the user in the recording unit 29 together with the date and time information.
  • the first biometric information pattern and the second biometric information pattern described above are extracted from the biometric information recorded in the recording unit 29 by appropriately selecting a specific period in the server 10 .
  • the configuration may be such that the portable terminal extracts and transmits this in response to a request from the server.
  • the lifestyle information acquisition unit 27 acquires the lifestyle information related to the daily lifestyle habits of the user of the portable terminal 20, the control unit 21 stores this acquired information together with the date and time information output by the clock unit 25. 29.
  • the user's position, movement, etc. are detected as life information, and based on the detection results, information such as whether the user is exercising such as jogging, sleeping, or working in the office.
  • life information acquisition unit 27 may collect the life information of the user of the mobile terminal 20 from information recorded in the server 30 or the like. If there is an index that indicates the user's stress, this stress index may be collected.
  • step S5 information such as lifestyle habits acquired in step S3 and the like is input to the inference model, inference results are obtained in order to improve the user's health condition, and the inference results are displayed on the display unit 24.
  • This inference may be performed in the mobile terminal 20 if the mobile terminal 20 has an inference engine. to the mobile terminal 20 . It should be noted that inference may be made using the biological information (vital information) acquired in step S1, not limited to lifestyle habits.
  • the server 10 may be requested to perform inference based on lifestyle habits, the inference result may be received from the server 10, and the inference result may be displayed on the display unit 24.
  • the server 10 is simply controlled to output the inference result immediately upon receiving a request, personal information such as health information can easily be stolen by a third party. Therefore, a message such as "Would you like to send this information to the doctor?" is displayed on the display screen 24a of FIG. Candidates should be verified. It is advisable to confirm the patient's intentions, such as the patient's consent to the contents of the transmission, before transmitting.
  • a cipher may be added to each communication and verbally communicated during the examination. Since it is difficult to create a cipher, it may be selected from pre-recorded candidates.
  • step S29 the server 10 requests the server to transmit terminal information for detecting the cause of symptoms.
  • the control unit 21 searches the recording unit 29 for information corresponding to the content of the request.
  • the requested information is personal information, it is preferable to provide a step of permitting the transmission of the personal information when transmitting the information to the server 10 .
  • step S7 only "search and display related information if there is an external request" was described, but in reality there are the following cases.
  • One is when the external request specifies information items, and the other is when the external request is vague, such as health-related or lifestyle information that can be sent.
  • the step of determining whether or not the information according to the request is recorded or whether it is possible to read out the recorded information;
  • the terminal user can easily check the filtering and information processing steps for information that is too related to personal privacy, and what is being sent in response to the request.
  • the processing for making the information easier to use may be performed on the terminal side, or may be performed by the system handled by the doctor (the server 10 or a device cooperating therewith).
  • a display is provided so that the items to be permitted to be transmitted can be selected.
  • the display screen 24b of FIG. 3 Prior to transmission, as shown in FIG. 3, "Would you like to send this information to the doctor?"
  • the display screen 24b of FIG. 3 has a display section for displaying specific numerical values of "recent living information data”, and the display screen 24c displays specific numerical values of "one year ago living information data”. Since there is a display unit that touches one of these, the one that is touched may be sent. In this way, not only the items but also information such as the date and time when the data was acquired are displayed, making it easy for the user to grasp which data is. Instead of individual data, information that can be read from it may be abstracted and transmitted. The place of purchase may also be deleted if it is not particularly necessary information.
  • a plurality of data of similar items may be grouped together in a specific period in an easy-to-collect manner, and steps such as abstraction and summarization may be provided. It may be possible to select whether or not to make a summary, or it may be possible to confirm by displaying whether or not the information has been collected into a summary and abstracted or anonymized. In this way, the user is allowed to send after carefully confirming.
  • steps S7 and S8 function as transmission candidate display steps for displaying transmission candidate information in which transmission-permitted information and transmission-not-permitted information can be selected before the switch display is operated.
  • This embodiment includes a receiving step in which the mobile terminal receives a request signal, a searching step in which, in response to the request, information corresponding to the request is searched from information recorded in a recording unit of the mobile terminal, a display control step of displaying information on a display unit of the mobile terminal and displaying a switch indicating whether or not to permit transmission of the displayed information to a requester of the displayed information.
  • the requested information to be retrieved from the information recorded in the recording unit of the mobile terminal includes at least a first biometric information pattern in a specific period going back at least a first time and a second biometric information pattern going back a second time in a specific period. and the information including the biological information pattern of .
  • the requested information to be retrieved from the information recorded in the recording unit of the mobile terminal may be information based on the time change of the biometric information pattern. Further, a stress determination step is provided to determine stress from the biometric information pattern recorded in the mobile terminal, and the requested information retrieved from the information recorded in the recording unit of the mobile terminal is stress information.
  • a specific timing or a specific situation For example, the user of the mobile terminal 10 may want to check past or present evidence such as biometric information or life information, or want to see health advice. In this case, the user operates the operation member such as the input unit 23 of the mobile terminal 20 . Also, the mobile terminal 20 or an external device (for example, the server 10) may automatically apply a trigger as a specific timing or specific situation. This automatic trigger may occur, for example, at predetermined time intervals, or may occur when biometric information or information representing lifestyle habits exceeds a predetermined value. As a result of this determination, if it is not the specific timing or the specific situation, the process returns to step S1.
  • specific timing or specific situation for example, the user of the mobile terminal 10 may want to check past or present evidence such as biometric information or life information, or want to see health advice. In this case, the user operates the operation member such as the input unit 23 of the mobile terminal 20 . Also, the mobile terminal 20 or an external device (for example, the server 10) may automatically apply a trigger as a
  • step S9 if the result of determination in step S9 is a specific timing or specific situation, evidence is displayed (S11).
  • the server 10 performs evidence determination by comparing representative values for a specific period (see S31 in FIG. 6B).
  • the control unit 21 causes the display unit 24 to display the result of the evidence comparison performed by the server 10 .
  • representative values for a specific period can be compared (for example, the biometric information and lifestyle information of the past one year ago are compared with the current biometric information and lifestyle information).
  • the control unit 21 or the like in the mobile terminal 20 may compare and display evidence in a specific period, or may display current or past evidence.
  • step S13 advice on improvement, medication advice, etc. are given (S13).
  • the server 10 prescribes from the supporting data (see S33 in FIG. 6B).
  • the control unit 21 causes the display unit 24 to display improvement advice and medication advice based on the prescription made by the server 10 . After this display, the process returns to step S1.
  • control unit 11 in server 10 controlling each unit in server 10 according to a program stored in the memory.
  • patient information is entered (S21).
  • the server 10 is assumed to be a server located within a medical facility such as a hospital, or a server located outside the medical facility or the like and communicably connected to devices within the medical facility or the like.
  • the information of the patient who came to the medical facility or the like to be examined is entered.
  • the patient information includes, for example, basic information necessary for patient diagnosis, such as name, age, sex, and pre-existing diseases.
  • a doctor or the like interviews the patient, performs necessary examinations, and examines the patient.
  • a doctor or the like may manually input necessary information into a computer such as a PC, or the information may be automatically input from a device or the like. Further, for example, voice data for a medical interview may be automatically converted into text data for input.
  • step S25 it is determined whether or not a doctor's interview or diagnosis has been made (S25).
  • the control unit 21 determines whether the doctor or the like conducted an interview or made a diagnosis. For example, a doctor may diagnose constipation due to lack of exercise as a result of an interview. If the result of determination in step S25 is that there has been no questioning or diagnosis, the process returns to step S21.
  • step S25 if the result of determination in step S25 is that there is an interview or diagnosis by a doctor, then a list of candidate causes is searched based on the contents of the interview, symptoms, and diagnosis results (S27).
  • the control unit 11 searches the disease-specific cause 19 d recorded in the recording/OB unit 19 .
  • the doctor can understand the symptoms of the patient by interviewing and examining the patient. If the cause of this symptom is known, the prescription should be made according to this cause. Therefore, in this step, the control unit 11 searches for possible causes recorded in the disease-specific causes 19d (see FIG. 7) of the recording/DB unit 19 in which symptoms and their causes are recorded.
  • a search may be made based on a hypothesis as to whether the lack of exercise is really the cause.
  • Candidate causes include at least one of stress, lack of exercise, irregular lifestyle, diet, and climate.
  • step S29 After searching for cause candidates from the list, the patient terminal is then requested to send terminal information for verifying the cause (S29).
  • step S27 by searching the disease-specific causes 19d, candidate causes are listed based on the patient's symptoms, and evidence data for verifying whether these causes are really the causes of the symptoms can be obtained (see FIG. 7). ). Therefore, in this step, the portable terminal 20 that the patient usually carries around is requested to transmit evidence data in order to verify the cause. If constipation due to lack of exercise is diagnosed as described above, the portable terminal 20 is requested to transmit evidence for verifying lack of exercise.
  • This step S29 functions as a communication step for notifying the mobile terminal of the patient that the first and second biometric information patterns are to be acquired, prior to acquisition of the first and second biometric information patterns.
  • step S29 evidence data transmitted from the mobile terminal 20 is received in response to a request from the server 10.
  • the portable terminal 20 transmits biological information and lifestyle information retroactively from the present to the past with respect to the requested evidence data.
  • a first biometric information pattern for example, a heart rate pattern
  • a second specific time period for example, several months one year ago
  • step S29 functions as a requesting step of searching for a candidate cause of a disease according to an inquiry or diagnosis by a doctor and requesting the patient's mobile terminal to send evidence data for verifying this candidate cause.
  • evidence data is a patient's biometric information data and/or lifestyle information data.
  • the evidence is determined by comparing the representative values for a specific period (S31).
  • S31 the representative values for a specific period.
  • the candidate cause was really the cause of the disease
  • the specific period is often unknown at the time of examination. Therefore, while comparing the evidence data for each predetermined period, we go back to the past and set the period when there is a clear difference between the present and the past as the specific period. If the above-mentioned constipation due to lack of exercise is diagnosed, in this step it is verified based on evidence data whether or not it is actually due to lack of exercise. Details of the operation in this step will be described later with reference to FIG.
  • step S31 compares the first biometric information pattern and the second biometric information pattern, and functions as a stress determination step of determining the patient's stress based on this comparison. Further, in step S31, when the evidence data is received from the portable terminal, the first pattern in the specific period of time going back for the first time and the second pattern of the specific period going back to the second time are compared for the evidence data. Acts as a comparison step to
  • the cause evidence judgment performed by comparing the representative values of the first and second biometric information patterns is based on the difference between the first and second biometric information patterns in the period retroactively for each candidate cause. to examine.
  • the time to go back is the time when differences in candidate causes are investigated and the differences are clear (for example, see S45 in FIG. 10).
  • the first and second biological information patterns are patterns that can be compared with the immediately preceding interval between the R-wave maximum values of the electrocardiogram. is.
  • the first and second biometric information patterns are patterns in which the peak value and frequency of blood pressure changes in a day can be compared. Moreover, determination of stress is performed by comparing the fluctuations of the first and second biometric information patterns.
  • the biometric information may include not only raw data but also processed or interpreted data.
  • step S31 After making a judgment using the evidence, it is then prescribed based on the backing data (S33).
  • the evidence determination in step S31 if the cause of the symptom assumed by the doctor can be verified, a prescription necessary to eliminate the cause of the symptom, such as medication, dietary guidance, exercise guidance, etc. out. If constipation due to lack of exercise is diagnosed as described above and the diagnosis is verified in step S31, the cause may be presumed to be lack of exercise and prescribed to the patient.
  • Step S31 functions as a determination step for determining the cause of the patient's disease from the cause candidates based on the result of comparison between the first pattern and the second pattern.
  • the patient himself/herself improves his/her life based on this cause, but the doctor may prescribe it, and in that case, it functions as a prescribing step of prescribing to the patient based on the determined cause.
  • step S35 when a large amount of data regarding patient symptoms and diagnostic results is collected, teacher data can be created based on these data. If a learning device for inference model generation is provided in the server 10, this learning device is used. If no learning device is provided in the server 10, a learning device provided in the external server 30 or the like is used. is used to generate an inference model for patient health improvement.
  • health advice can be given to the user in the mobile terminal 20 (see S5 in FIG. 6A). Generation of an inference model will be described later with reference to FIG.
  • the server 10 can be connected to a large number of mobile terminals 20, and can collect evidence data (terminal information) from these mobile terminals 20. Furthermore, the server 10 may also collect evidence data (terminal information) from other similar servers. By collecting a large amount of evidence data, it becomes possible to generate a highly reliable inference model. After creating an inference model in step S35, the process returns to step S21.
  • the mobile terminal 20 records the user's biometric information and lifestyle on a daily basis (see S1 and S3 in FIG. 6A).
  • the user searches for cause candidates to confirm the doctor's diagnosis result (S23 to S27 in FIG. 6B), and checks whether the cause candidate is really the cause.
  • the terminal 20 is requested to provide evidence data (S7 in FIG. 6A, S29 in FIG. 6B).
  • the server 10 analyzes the evidence data provided from the mobile terminal 20, and presents medication advice to the user based on the results of this analysis and corroborated data, as well as improvement advice such as diet/exercise advice. (S23 in FIG. 6B, S13 in FIG. 6A). Therefore, in the present embodiment, the prescription based on the doctor's examination or the like is verified based on the evidence data recorded in the user's terminal mobile phone 20, so that a reliable prescription can be made.
  • an inference model is generated based on diagnostic results that are backed up by the user's evidence data (S35 in FIG. 6B). Then, the user can receive advice for improving the health condition on a daily basis with respect to this inference model and data such as lifestyle habits (S5 in FIG. 6A). For this reason, it is possible to receive advice for improving the state of health using a highly reliable inference model.
  • step S27 and S29 of FIG. 6B the patient terminal is requested to provide evidence data in order to detect hypotheses based on medical examination by a doctor or the like.
  • the chart shown in FIG. 7 shows the relationship between the cause expected from the patient's symptoms, the evidence data for verifying this cause, and the equipment for acquiring this evidence data.
  • the logic of the idea is specified in a table format, but it is not necessary to organize it in a table format (database format, so to speak), and it is possible to branch and judge based on some rule (following a set program). , or derived from a learned inference model.
  • unexpected factors may be intertwined or unexpected events may trigger the onset of the disease. degree), etc. may also be determined. However, in most cases, an interview is enough.
  • this table shows items that are considered to be the cause of illness, but are difficult to obtain as evidence only at the time of examination (that is, data that can be easily obtained on a daily basis), changes over time in interviews, and interactions with other people. Priority is given to items that are difficult to compare.
  • the patient's symptoms are listed in the leftmost column of the table, and to the right of it are listed candidates for the cause of the symptoms.
  • the rightmost column lists the equipment used to detect the evidence data. For example, if the patient's symptom is a digestive system such as abdominal pain, constipation, etc., the cause of this symptom may be (1) lack of exercise, (2) food intake, (3) irregular lifestyle, ( 4) Stress is assumed.
  • the pedometer (registered trademark) data can be obtained from the pedometer (registered trademark) app on the smartphone used by the patient, and the heart rate can be obtained from the smart watch used by the patient. can be obtained.
  • Other examples can be understood from FIG. 7, and detailed description thereof will be omitted.
  • the heart rate can be obtained by a smartwatch with a sleep measurement function, a fitness tracker, etc.
  • An optical heart rate monitor such as a smart watch projects green light onto the skin surface of the user's arm, measures the blood flow by measuring the reflected light, and calculates the heart rate from the increase or decrease in the blood flow.
  • smart watches and the like are equipped with an "acceleration sensor" that detects the movement of the arm, and both of these are used to measure and analyze sleep conditions. Based on this analysis, two states can be distinguished: the waking state (wake state) and the sleeping state (sleep state). Based on the analysis, sleep quality can also be divided into levels such as "light sleep", “deep sleep”, “REM sleep”, and "wake”.
  • the doctor 53 explains to the patient the cause of the patient's disease (the occurrence of polyps in this example) while looking at the PC 10A.
  • the doctor 53 assumes several candidates for the cause of the patient's disease (occurrence of polyps), and provides evidence data (in this example, data on stress and sleep time) is transmitted from the patient's portable terminal 20 to the server 10 (see S27 and S29 in FIG. 6B and S7 in FIG. 6A).
  • the server 10 compares the representative values of the specific period and determines whether the assumed cause is certain (see S31 and S33 in FIG. 6B).
  • the representative value of a specific period may be an individual value of this period, an average value of the period, and may be a time zone as well as a day unit.
  • the graph Gr5 on the PC 10A shows the stress signal SigLY for a certain period of the last year and the stress signal SigC for a recent certain period as the specific period. Blood pressure and an electrocardiogram are used as this stress signal, as shown in FIG.
  • the PC 110A shows a graph Gr6 indicating sleep hours from before awareness to the latest. Before I realized it, I went to bed before 11:00 pm and woke up after 7:00 am. However, these days, he goes to bed after 11:00 pm and wakes up before 7:00 am, so it is clear that he sleeps less these days.
  • Physician 53 uses graphs Gr5 and Gr6 to compare past and present evidence regarding stress and sleep time, and finds that stress is the cause of the patient's disease (occurrence of polyps). Once the cause is known, three pieces of advice can be given to the patient to reduce stress: (1) take deep breaths, (2) exercise moderately, and (3) get good quality sleep. can.
  • FIG. 9 shows an example of a biological information pattern displayed on the portable terminal 20 of the patient.
  • This screen may be shown to a doctor for diagnosis, and the same display as this can be displayed on the doctor's terminal.
  • the mobile terminal 20 records it in the recording unit 29.
  • the requested information is retrieved from the information, and the retrieved information is displayed on the display unit 24 of the portable terminal 20.
  • a graph is displayed instead of the numerical value itself.
  • This embodiment includes an example of an information sharing method having a step of graphing the data as graphing occurs.
  • the display screen 24d shows an example of causes of diseases, and in the example of FIG. 9, shows that one of the causes of "polyps" is "stress".
  • the display screen 24e shows a display example of evidence, and in the example shown in FIG. 9, the graph Gr5 shown in FIG. 8 is displayed. In this example, last year's stress versus recent stress is displayed, so the patient knows what the cause is and is motivated to follow the advice.
  • a display screen 24f shows a countermeasure, and this screen describes "Jump to guide by clicking". When the patient clicks on this screen 24f, the above-mentioned advices (1) to (3) from the doctor are displayed. Advice may also be displayed by inference using an inference model generated by learning (see FIG. 11, which will be described later).
  • step S31 the evidence data in the past patient's symptom-free period is compared with the evidence data in the symptom-producing period to determine the cause of the symptom, disease, etc. do.
  • determination is first started for each cause candidate (S41).
  • S41 determines whether there may be a plurality of candidates for the cause of symptoms (including diseases and the like).
  • determination is started for each of the plurality of candidates for causes in order. This order may be determined in advance for each symptom. Alternatively, the symptoms may be determined in detail, and candidates may be narrowed down according to the symptoms determined in detail before determination may be made.
  • the control unit 11 selects the evidence data for the same period as the present and one month ago from among the received evidence data. Extract and compare.
  • the period for comparison for example, a period of about one week or the like may be determined, and the period may be appropriately adjusted to the extent that characteristics such as symptoms can be recognized. Also, instead of comparing the tendency of individual data in a specific period (month/day (hour) width), an average value, a representative value, or the like in a specific period may be selected.
  • control unit 11 determines whether or not the value of the candidate cause is worse now than in the past. For example, if the cause candidate is stress, it is determined whether or not it is worse now than in the past.
  • the differences may be stored and the most significant difference may be used as evidence.
  • the cause candidate is used as evidence candidate (S47). If the cause candidate is worsening than in the past, it is regarded as an evidence candidate because the cause candidate may be the cause of the patient's disease or the like.
  • step S47 If an evidence candidate is selected in step S47, or if the result of determination in step S45 is that there is no difference in the direction of deterioration, then it is determined whether or not confirmation has been made up to one year ago (S49).
  • step S43 after comparing the evidence data of one month ago with the present evidence data, in step S51, the object of comparison is changed to one month before, and the comparison is performed one month at a time. In this step, it is determined whether or not the comparison target goes back one year.
  • step S49 if confirmation has not been made up to one year ago, the comparison target is further traced back one month (S51). If the comparison target is further traced back one month, the process returns to step S41 and the above-described processing is repeated.
  • one year before in step S49 and one month before in step S51 are examples, and these numerical values may be appropriately changed according to characteristics such as symptoms (disease).
  • step S49 If the result of determination in step S49 is that confirmation has been completed up to one year ago, it is next determined whether or not all candidate causes have been confirmed (S53). In step S41, possible cause candidates are listed, and determination is started for each cause candidate. Here, the control unit 11 determines whether or not the processes in steps S41 to S51 have been executed for all of the listed cause candidates.
  • step S53 If the result of determination in step S53 is that confirmation has not been completed for all candidate causes, another candidate is determined (S55).
  • the control unit 11 determines the next cause candidate according to the order of the cause candidates for which determination has not been completed. After determining the cause candidates to be determined next, the process returns to step S41.
  • step S53 determination is made starting with the candidates (S57).
  • step S47 evidence data deteriorated are taken as evidence candidates.
  • the evidence that caused the symptom (disease) is selected from the evidence extracted as candidates by the control unit 11 .
  • the cause candidates are determined according to the characteristics of each symptom (disease), and, for example, a cause with a large degree of deterioration in evidence data may be selected.
  • the evidence data for the cause candidate in the current and past specific periods are compared, and whether or not the evidence data is deteriorating is determined by changing the period to be compared. I am judging.
  • each cause candidate is similarly compared and determined. By performing these treatments, it is possible to find out the cause of worsening of the patient's symptoms, and to give an appropriate prescription.
  • the first and second biological information pattern types are selected, and the selected types of the first and second Differences are examined for the second biometric pattern. If a difference is found, the cause of the disease can be determined based on the type of evidence data, and appropriate prescription can be made.
  • step S35 generation of an inference model in step S35 (see FIG. 6B) will be described using FIG.
  • User's evidence data is accumulated in the server 10 from many mobile terminals 20 . This large body of evidence data can be used to generate inference models for symptom inference and treatment.
  • Fig. 11 shows an example of file-managed evidence data.
  • Graph Gr5 shows the stress data of a user who had a polyp removed by gastric endoscopy and underwent dietary restrictions. In this example, the change of the stress signal SigLY of the last year and the stress signal of the recent SigC is shown.
  • graph Gr6 shows the data of the user who was reexamined in the colonoscopy. This example also shows the change in sleep hours between last year and recent years. In these examples, since similar data are collected by standardizing them as one year ago or the like, the data can be easily collected and compared. In addition, the influence of seasonal differences is reduced.
  • Such graphs may be displayed on the terminal used by the patient.
  • a doctor When displayed as a graph, it is easier for a doctor to grasp a specific image rather than simply showing a collection of data, and for a doctor to look at the display and help with diagnosis.
  • an indication to that effect should be communicated to the physician.
  • the date and time data of data acquisition is also displayed as evidence.
  • the horizontal axis is age, gender, etc., but it is not limited to this, and it is better to include factors that are likely to affect the causes of symptoms and perform cause analysis. For example, genetic information may be incorporated.
  • the lower part of FIG. 11 shows the configuration for learning using evidence data and generating an inference model.
  • symptoms and treatment information for example, polypectomy and dietary restrictions in Gr5 gastric endoscopy, and reexamination in Gr6 colonoscopy
  • Create teacher data by annotating.
  • the teacher data is created, the evidence data is input to the input layer In of the neural network, deep learning is performed so that the symptom and treatment information is output to the output layer Out, and the weighting of each intermediate layer is calculated. and generate an inference model.
  • inference can be made in step S5 (see FIG. 6A), and improvement advice and medication advice can be given in S13.
  • improvement advice and medication advice can be given in S13.
  • FIG. 11 illustrates an example in which teacher data is created based on biometric information and an inference model is generated, the present invention is not limited to this, and an inference model for giving health advice is generated based on lifestyle information. Alternatively, an inference model may be generated using both biometric information and lifestyle information.
  • Deep learning is a multilayer structure of the process of "machine learning” using neural networks.
  • a typical example is a "forward propagation neural network” that sends information from front to back and makes decisions.
  • the simplest forward propagation neural network consists of an input layer composed of N1 neurons, an intermediate layer composed of N2 neurons given by parameters, and N3 neurons corresponding to the number of classes to be discriminated. It suffices if there are three output layers composed of neurons.
  • the neurons of the input layer and the intermediate layer, and the intermediate layer and the output layer are connected by connection weights, respectively, and the intermediate layer and the output layer are added with bias values, so that logic gates can be easily formed.
  • the neural network may have three layers for simple discrimination, but by increasing the number of intermediate layers, it is also possible to learn how to combine multiple feature values in the process of machine learning. In recent years, 9 to 152 layers have become practical from the viewpoint of the time required for learning, judgment accuracy, and energy consumption.
  • a process called “convolution” that compresses the feature amount of an image may be performed, and a “convolution neural network” that operates with minimal processing and is strong in pattern recognition may be used.
  • a "recurrent neural network” fully-connected recurrent neural network
  • which can handle more complicated information and can handle information analysis whose meaning changes depending on the order and order, may be used in which information flows in both directions.
  • NPU neural network processing unit
  • machine learning such as support vector machines and support vector regression.
  • the learning involves calculation of classifier weights, filter coefficients, and offsets, and there is also a method using logistic regression processing. If you want a machine to judge something, you have to teach the machine how to judge.
  • a method of deriving image determination by machine learning is used.
  • a rule-based method that applies rules acquired by humans through empirical rules and heuristics may be used.
  • Risk factors for gastrointestinal disease are stress and lifestyle.
  • the left column of FIG. 12 shows the risk factors for gastric and esophageal diseases as digestive diseases. These risk factors include stress and lifestyle. These lifestyle habits include fatty foods, sweet foods, too hot foods, stimulants such as caffeine and spices, excessive drinking, smoking, and Helicobacter pylori.
  • Stress as a risk factor is affected by life events.
  • stress is determined based on biometric information, but methods other than biometric information for determining stress based on life events are also known.
  • the upper right column of FIG. 12 shows the degree of stress for events in life. For example, the death of a spouse has a stress level of 100, divorce has a stress level of 73, and separation has a stress level of 66.
  • Life events can be determined based on the action history, consumption behavior, SNS, photos, etc. recorded in the mobile terminal 20 . Further, determination may be made based on information or the like posted to the server 30 by a user or the like, not limited to the mobile terminal 20 .
  • lifestyle habits can be a factor in illness.
  • the lower right column of FIG. 12 shows the relationship between lifestyle habits and the occurrence of cancer.
  • lifestyle habits that cause cancer include smoking (active), infectious factors (eg, Helicobacter pylori), alcohol consumption, salt intake, lack of exercise, and the like.
  • These lifestyle habits can be determined based on the action history, consumption behavior, SNS, photos, etc. recorded in the mobile terminal 20, the server 30, and the like.
  • the weight and the like may be acquired from a weight scale linked with the mobile terminal 20 .
  • lifestyle information and biometric information may be acquired based on these information.
  • test kits for testing infectious diseases such as Helicobacter pylori mentioned above can be purchased by general users through electronic transactions conducted through the Internet, etc., based on the purchase history of these and the history of diagnostic services using test kits, etc. Lifestyle information and the like may be acquired.
  • risk factors for stomach and esophageal diseases are related to stress and lifestyle habits.
  • the evidence data that caused the patient's disease or the like is collected from the patient's mobile terminal 20, and the cause is explored (see S25 to S33 in FIG. 6B). ). Therefore, in addition to the collected biometric information, lifestyle information can be used to generate an inference model capable of giving advice to the user regarding tendencies (see, for example, S35 in FIG. 6B and FIG. 11).
  • the information processing method acquires a first biological information pattern in a specific period going back a first time and a second biological information pattern going back a specific period of time going back a second time. and a stress determination step of comparing the first biometric information pattern and the second biometric information pattern and determining the stress of the patient based on this comparison. .
  • a stress determination step of comparing the first biometric information pattern and the second biometric information pattern and determining the stress of the patient based on this comparison.
  • the information processing method searches for a candidate causing a disease according to an interview or diagnosis by a doctor, and requests the patient's portable terminal to send evidence data for verifying this candidate cause.
  • request step (see, for example, S27 and S29 in FIG. 6B), and when the evidence data is received from the mobile terminal, a first pattern in a specific period going back a first time and a second time going back for the evidence data
  • a comparison step of comparing a second pattern in a specific period see, for example, S31 in FIG. 6B
  • It has a prescribing step see, for example, S35 in FIG. 6B of determining and prescribing to the patient.
  • the evidence data for verifying the candidate causing the disease is collected from the patient's portable terminal and verified, the evidence data can be efficiently collected.
  • highly accurate prescription can be performed based on this evidence data.
  • the information sharing method of the mobile terminal includes a receiving step (for example, see S7 in FIG. 6A) in which the mobile terminal receives a request signal, and a recording unit of the mobile terminal in response to the request signal
  • a search step (see, for example, S7 in FIG. 6A) for searching for requested information from among the recorded information, displaying the searched information on the display unit of the mobile terminal, and identifying the requester of the displayed information
  • It has a display control step (see, for example, S7 and S8 in FIG. 6A) for displaying a switch indicating whether or not to permit transmission.
  • the above request signal is a signal requesting provision of medical information from the server 10 connected to a doctor or the like.
  • the medical information includes, for example, evidence data as shown in FIG. 7, biological information, lifestyle information, and the like.
  • the requester for example, the server 10
  • the provider A person such as the owner of the mobile device
  • the owner of the mobile terminal or the like can control the provision of personal information such as medical information to the outside.
  • biometric information and evidence data on lifestyle habits were acquired, but either one may be acquired. Also, biometric information and evidence data relating to lifestyle habits may be obtained and both evidence data may be used for determination.
  • the server 10 is explained as one server, but it may be two or more servers, and a plurality of servers may cooperate. Furthermore, it may be configured integrally with the external server 30 .
  • the controllers 11 and 21 have been described as devices configured from CPUs, memories, and the like. However, in addition to being configured as software by a CPU and a program, part or all of each part may be configured as a hardware circuit, and is described in Verilog, VHDL (Verilog Hardware Description Language), etc. A hardware configuration such as a gate circuit generated based on a program language may be used, or a hardware configuration using software such as a DSP (Digital Signal Processor) may be used. Of course, these may be combined as appropriate.
  • DSP Digital Signal Processor
  • control units 11 and 21 are not limited to CPUs, and may be elements that function as controllers, and the processing of each unit described above may be performed by one or more processors configured as hardware.
  • each unit may be a processor configured as an electronic circuit, or may be each circuit unit in a processor configured with an integrated circuit such as an FPGA (Field Programmable Gate Array).
  • FPGA Field Programmable Gate Array
  • a processor composed of one or more CPUs may read and execute a computer program recorded on a recording medium, thereby executing the function of each unit.
  • the server 10 includes a control unit 11, a communication unit 12, an inquiry input unit 13, a DB search unit 14, a clock unit 15, a biological information comparison unit 16, a stress determination unit 17, a lifestyle It has been described as having the determination unit 18 and the recording/DB unit 19 . However, they do not need to be provided in an integrated device, and the above-described units may be distributed as long as they are connected by a communication network such as the Internet. Also, some of the functions of the respective units described above may be distributed and arranged in the mobile terminal 20 and/or the server 30 .
  • the mobile terminal 20 includes a control unit 21, a communication unit 22, an input unit 23, a display unit 24, a clock unit 25, a biological information acquisition unit 26, a life information acquisition unit 27, an advice unit 28 and a recording unit 29 are described.
  • the above-described units may be distributed as long as they are connected by a communication network such as the Internet.
  • some of the functions of the respective units described above may be distributed and arranged in the server 10 and/or the server 30 .
  • control described mainly in the flowcharts can often be set by a program, and may be stored in a recording medium or recording unit.
  • the method of recording in the recording medium and the recording unit may be recorded at the time of product shipment, using a distributed recording medium, or downloading via the Internet.
  • the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the spirit of the present invention at the implementation stage. Also, various inventions can be formed by appropriate combinations of the plurality of constituent elements disclosed in the above embodiments. For example, some components of all components shown in the embodiments may be omitted. Furthermore, components across different embodiments may be combined as appropriate.

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Abstract

Provided are an information collection method and an information collection device with which lifestyle habit data can be efficiently selected on the basis of a diagnosis result from a doctor or the like. The information collection method includes an acquisition step (S29) for acquiring a first biological information pattern in a specific period going back a first time period and a second biological information pattern in a specific period going back a second time period, and a stress determination step (S31) in which the first biological information pattern and the second biological information pattern are compared and a patient's stress is determined on the basis of the comparison.

Description

情報収集方法、情報収集装置、および携帯端末の情報共有方法Information collection method, information collection device, and information sharing method for mobile terminal
　本発明は、患者等を診察するにあたって、症状の原因となった生活習慣等に関する情報を収集することができる情報収集方法、情報収集装置、および携帯端末の情報共有方法に関する。 The present invention relates to an information collection method, an information collection device, and an information sharing method for mobile terminals that can collect information on lifestyle habits, etc. that have caused symptoms when examining a patient or the like.
　生活習慣病は、患者の生活習慣と密接な関係があることが知られており、収集されたユーザの行動を分析して生成された習慣データに基づいて、ユーザが疾患の危険群であるか否かを診断するための診断装置および診断管理装置等が提案されている（特許文献１参照）。この特許文献１には、１つ以上のセンサから検出されたセンサーデータを分析して、ユーザの習慣データを生成し、この生成された習慣データと正常者の生活習慣データである診断用データとを比較して、疾患の危険群であるか否かを診断することが開示されている。 It is known that lifestyle-related diseases are closely related to the lifestyle habits of patients. A diagnostic device, a diagnostic management device, and the like for diagnosing whether or not there is a problem have been proposed (see Patent Document 1). In Patent Document 1, sensor data detected from one or more sensors is analyzed to generate user habit data, and the generated habit data and diagnostic data, which is normal person's lifestyle habit data, are combined. to diagnose whether one is in a disease risk group.
特表２０１６－５２９６０６号公報Japanese Patent Publication No. 2016-529606
　上述の特許文献１に開示の新断装置では、ユーザの習慣データを求め、このデータと診断用のデータを比較することによって、疾患の危険度を判定することが開示されている。すなわち、特許文献１では、習慣データを求めることが開示されているが、医師等が患者等を診察した際の診断結果に基づいて、効率的に患者の習慣データを集めて、疾患の原因となった生活習慣が突き止めることについては、何ら記載されていなかった。 The new device disclosed in the above-mentioned Patent Document 1 discloses determining the degree of disease risk by obtaining user's habit data and comparing this data with diagnostic data. That is, although Patent Document 1 discloses obtaining habit data, it is possible to efficiently collect patient habit data based on the diagnosis results when a doctor or the like examines the patient, etc., and identify the cause of the disease. There was no mention of what a new lifestyle would pinpoint.
　本発明は、このような事情を鑑みてなされたものであり、医師等の診断結果に基づいて効率的にエビデンスデータを選択することのできる情報収集方法、情報収集装置、および携帯端末の情報共有方法を提供することを目的とする。 The present invention has been made in view of such circumstances, and provides an information collection method, an information collection device, and information sharing of a mobile terminal that enable efficient selection of evidence data based on the diagnosis results of a doctor or the like. The purpose is to provide a method.
　上記目的を達成するため第１の発明に係る情報収集方法は、第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンを取得する取得ステップと、上記第１の生体情報パターンと第２の生体情報パターンを比較し、この比較に基づいて、患者のストレスを判定するストレス判定ステップと、を有する。 In order to achieve the above object, an information gathering method according to a first invention acquires a first biometric information pattern in a specific period going back in time and a second biometric information pattern in a specific period going back in time. and a stress determination step of comparing the first biometric information pattern and the second biometric information pattern and determining the stress of the patient based on the comparison.
　第２の発明に係る情報収集方法は、上記第１の発明において、上記患者の現在の病状情報に対して、上記第１の生体情報パターンと、上記第２の生体情報パターンを関連づけて記録する記録ステップを、さらに具備する。
　第３の発明に係る情報収集方法は、上記第１の発明において、上記第１および第２の生体情報パターンの取得に先立って、上記患者の有する携帯端末に対して上記第１および第２の生体情報パターンを取得する旨を通知する通信ステップを具備する。 An information collecting method according to a second invention is the information collecting method according to the first invention, in which the first biological information pattern and the second biological information pattern are recorded in association with the current medical condition information of the patient. A recording step is further provided.
An information gathering method according to a third invention is the information collection method according to the first invention, wherein prior to obtaining the first and second biometric information patterns, the first and second information is sent to the mobile terminal of the patient. It comprises a communication step of notifying that the biometric information pattern is to be acquired.
　第４の発明に係る情報収集方法は、上記第１の発明において、上記特定期間において、上記第１および第２の生体情報パターンの代表値を比較することによって行う原因エビデンス判定は、候補となる原因毎に上記遡った時期における上記第１および第２の生体情報パターンの差異を調べる。
　第５の発明に係る情報収集方法は、上記第４の発明において、上記原因エビデンス判定において、上記遡る時期は、候補となる原因の差異を調べ、差異が明確な時期とする。 An information gathering method according to a fourth invention is the first invention, wherein the cause evidence determination performed by comparing the representative values of the first and second biological information patterns in the specific period is a candidate. A difference between the first and second biometric information patterns in the retroactive period is examined for each cause.
An information gathering method according to a fifth aspect of the invention is the fourth aspect of the invention, wherein in the causal evidence determination, the retroactive time is set to a time when differences in candidate causes are investigated and the differences are clear.
　第６の発明に係る情報収集方法は、上記第１の発明において、医師の問診または診断に従って選択された候補となる原因に基づいて、上記第１および第２の生体情報パターンの種類を選択し、この選択された種類の上記第１及び第２の生体情報パターンについて上記差異を調べる。
　第７の発明に係る情報収集方法は、上記第１の発明において、上記ストレスの判定結果に基づいて、上記患者への治療方法を決定する。 An information gathering method according to a sixth aspect of the invention, in the first aspect of the invention, selects the types of the first and second biological information patterns based on the candidate cause selected according to the doctor's interview or diagnosis. , to examine the difference for the first and second bioinformation patterns of this selected type.
An information gathering method according to a seventh aspect of the present invention is the information gathering method according to the first aspect of the invention, in which a treatment method for the patient is determined based on the determination result of the stress.
　第８の発明に係る情報収集方法は、上記第１の発明において、上記第１及び第２の生体情報パターンは、心電図のＲ波最大値時点間の 間隔を、直前に求められたものと比較可能なパターンである。
　第９の発明に係る情報収集方法は、上記第１の発明において、上記第１および第２の生体情報パターンは、一日の血圧変化のピーク値と回数が比較可能なパターンである。
　第１０の発明に係る情報収集方法は、上記第１の発明において、上記ストレスの判定は、上記第１および第２の生体情報パターンの揺らぎを比較する。 An information gathering method according to an eighth invention is the information gathering method according to the first invention, wherein the first and second biological information patterns compare the interval between the R-wave maximum values of the electrocardiogram with the one obtained immediately before. possible pattern.
An information collecting method according to a ninth aspect of the invention is the information gathering method according to the first aspect, wherein the first and second biological information patterns are patterns in which the peak values and the number of blood pressure changes in a day can be compared.
An information gathering method according to a tenth invention is the information collection method according to the first invention, wherein the determination of the stress compares fluctuations of the first and second biometric information patterns.
　第１１の発明に係る情報収集装置は、第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンを取得する取得部と、上記第１の生体情報パターンと第２の生体情報パターンを比較し、この比較に基づいて、患者のストレスを判定するストレス判定部と、と有する。 An information collecting apparatus according to an eleventh aspect of the present invention includes an acquisition unit that acquires a first biological information pattern in a specific period that goes back a first time, and a second biological information pattern that goes back in a second time in a specific period, and a stress determination unit that compares the first biometric information pattern and the second biometric information pattern and determines the patient's stress based on this comparison;
　第１２の発明に係る情報収集方法は、医師の問診または診断に従って、疾患の原因となる候補を検索し、この原因候補を検証するためのエビデンスデータの送付を患者の携帯端末に要求する要求ステップと、上記携帯端末から上記エビデンスデータを受信すると、該エビデンスデータについての、第１の時間遡る特定期間における第１のパターンと、第２の時間遡る特定期間における第２のパターンを比較する比較ステップと、上記第１のパターンと第２のパターンの比較結果に基づいて、上記原因候補の中から上記患者の疾患の原因を決定する決定ステップと、を有する。 An information gathering method according to a twelfth aspect of the invention includes a request step of searching for a candidate causing a disease according to an interview or diagnosis by a doctor and requesting the patient's portable terminal to send evidence data for verifying the candidate cause. and, when the evidence data is received from the portable terminal, a comparison step of comparing a first pattern in a specific period of time preceding a first time period and a second pattern of the evidence data in a specific period of time preceding a second time period. and a determination step of determining the cause of the patient's disease from among the cause candidates based on the result of comparing the first pattern and the second pattern.
　第１３の発明に係る情報収集方法は、上記第１２発明において、上記エビデンスデータは、上記患者の生体情報データおよび／または生活習慣情報データである。
　第１４の発明に係る情報収集方法は、上記第１２の発明において、上記原因候補は、ストレス、運動不足、不規則な生活、摂取物、気候の内の少なくとも１を含む。
　第１５の発明に係る情報収集方法は、上記第１２の発明において、決定された上記原因に基づいて、上記患者に処方する処方ステップを有し、上記患者への処方を、上記携帯端末に送信する。 An information gathering method according to a thirteenth aspect of the invention is the twelfth aspect of the invention, wherein the evidence data is biological information data and/or lifestyle information data of the patient.
A fourteenth aspect of the present invention is an information gathering method according to the twelfth aspect, wherein the candidate cause includes at least one of stress, lack of exercise, irregular life, intake, and climate.
An information gathering method according to a fifteenth invention, in the twelfth invention, has a prescription step of prescribing to the patient based on the determined cause, and transmitting the prescription to the patient to the portable terminal. do.
　第１６の発明に係る情報収集装置は、医師の問診または診断に従って、疾患の原因となる候補を検索する検索部と、上記原因候補を検証するためのエビデンスデータの送信を患者の携帯端末に要求する通信部と、上記携帯端末から上記エビデンスデータを受信すると、該エビデンスデータについての、第１の時間遡る特定期間における第１のパターンと、第２の時間遡る特定期間における第２のパターンを比較する比較部と、を有し、上記第１のパターンと第２のパターンの比較結果に基づいて、上記原因候補の中から上記患者の疾患の原因が決定されると、上記通信部は上記患者に対する処方を上記携帯端末に送信する。 An information collection device according to a sixteenth aspect of the present invention includes a retrieval unit that retrieves a candidate for the cause of a disease according to an interview or diagnosis by a doctor, and requests a patient's portable terminal to transmit evidence data for verifying the candidate for the cause. and a communication unit that receives the evidence data from the portable terminal, and compares a first pattern in a specific period going back a first time period and a second pattern in a specific period going back a second time period for the evidence data. and a comparison unit for determining the cause of the patient's disease from among the candidate causes based on the comparison result of the first pattern and the second pattern, the communication unit determining the cause of the patient's disease to the portable terminal.
　第１７の発明に係る携帯端末の情報共有方法は、携帯端末が依頼信号を受信する受信ステップと、上記依頼信号に応じて、上記携帯端末の記録部に記録された情報の中から依頼に応じた情報を検索する検索ステップと、上記検索された情報を上記携帯端末の表示部に表示すると共に、上記表示された情報の依頼元に送信を許可するか否かのスイッチ表示を行う表示制御ステップと、を有する。 An information sharing method for a mobile terminal according to a seventeenth invention comprises a receiving step in which the mobile terminal receives a request signal; and a display control step of displaying the retrieved information on the display unit of the mobile terminal and displaying a switch indicating whether or not to permit transmission of the displayed information to the requester of the displayed information. and have
　第１８の発明に係る携帯端末の情報共有方法は、上記第１７の発明において、上記スイッチ表示の操作前に、送信を許可する情報と許可しない情報を選択可能とした送信候補情報を表示する送信候補表示ステップをさらに有する。
　第１９の発明に係る携帯端末の情報共有方法は、上記第１７の発明において、上記携帯端末の記録部に記録された情報の中から検索する、依頼に応じた情報としては、少なくとも第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンと、を含む情報である。 An information sharing method for a mobile terminal according to an eighteenth aspect is the method according to the seventeenth aspect, wherein transmission candidate information is displayed in which transmission is permitted and information is not permitted to be selected prior to operation of the switch display. It further has a candidate display step.
In the information sharing method for a mobile terminal according to a nineteenth invention, in the seventeenth invention, at least the first The information includes a first biological information pattern in a specific time period that goes back in time and a second biological information pattern in a second specific period that goes back in time.
　第２０の発明に係る携帯端末の情報共有方法は、上記第１７の発明において、上記携帯端末の記録部に記録された情報の中から検索する、依頼に応じた情報としては、生体情報パターンの時間変化に基づいた情報である。
　第２１の発明に係る携帯端末の情報共有方法は、上記第１７の発明において、上記携帯端末の上記記録部に記録された生体情報パターンからストレスを判定するストレス判定ステップを有し、上記携帯端末の記録部に記録された情報の中から検索する、依頼に応じた情報としては、ストレス情報である。 According to a twentieth aspect of the invention, there is provided an information sharing method for a mobile terminal according to the seventeenth aspect, wherein the requested information to be retrieved from the information recorded in the recording unit of the mobile terminal is a biometric information pattern. It is information based on time change.
A method for sharing information on a mobile terminal according to a twenty-first invention is the method according to the seventeenth invention, further comprising: a stress determination step of determining stress from the biometric information pattern recorded in the recording unit of the mobile terminal; The requested information to be retrieved from the information recorded in the recording unit is stress information.
　本発明によれば、医師等の診断結果に基づいて効率的にエビデンスデータを選択することのできる情報収集方法、情報収集装置、および携帯端末の情報共有方法を提供することができる。 According to the present invention, it is possible to provide an information collection method, an information collection device, and an information sharing method for mobile terminals that enable efficient selection of evidence data based on the diagnosis results of doctors and the like.
本発明の一実施形態に係る情報収集システムの構成を示すブロック図である。1 is a block diagram showing the configuration of an information collection system according to one embodiment of the present invention; FIG. 本発明の一実施形態に係る情報収集システムを医師と患者が使用している様子を示す図である。It is a figure which shows a mode that a doctor and a patient are using the information collection system which concerns on one Embodiment of this invention. 本発明の一実施形態に係る情報収集システムの携帯端末における表示画面を示す図である。It is a figure which shows the display screen in the portable terminal of the information collection system which concerns on one Embodiment of this invention. 本発明の一実施形態に係る情報収集システムにおいて、収集される脈波等の生体信号の変化を示すグラフである。4 is a graph showing changes in biological signals such as pulse waves collected in the information collection system according to one embodiment of the present invention. 本発明の一実施形態に係る情報収集システムにおいて、収集される脈波等を処理して得られるパワースペクトラムを示すグラフである。4 is a graph showing a power spectrum obtained by processing collected pulse waves and the like in the information collection system according to one embodiment of the present invention. 本発明の一実施形態における情報収集システムにおいて、血圧の変化とストレスの関係を示すグラフである。5 is a graph showing the relationship between changes in blood pressure and stress in the information collection system according to one embodiment of the present invention. 本発明に一実施形態に係る情報収集システムにおいて、携帯端末の動作を示すフローチャートである。4 is a flow chart showing the operation of the mobile terminal in the information collection system according to one embodiment of the present invention. 本発明に一実施形態に係る情報収集システムにおいて、サーバの動作を示すフローチャートである。4 is a flow chart showing the operation of the server in the information collection system according to one embodiment of the present invention; 本発明に一実施形態に係る情報収集システムにおいて、症状と、この症状のエビデンスデータとの関係を示す図表である。4 is a chart showing the relationship between symptoms and evidence data of the symptoms in the information collection system according to one embodiment of the present invention. 本発明の一実施形態に係る情報収集システムを用いて、医師等が症状の原因としての生活習慣を検討している様子を示す図である。FIG. 2 is a diagram showing a state in which a doctor or the like is examining lifestyle habits as a cause of symptoms using the information collection system according to one embodiment of the present invention; 本発明の一実施形態に係る情報収集システムの携帯端末における表示画面を示す図である。It is a figure which shows the display screen in the portable terminal of the information collection system which concerns on one Embodiment of this invention. 本発明に一実施形態に係る情報収集システムにおいて、サーバにおける「特定期間の代表値比較でエビデンス判定」の動作を示すフローチャートである。4 is a flow chart showing the operation of the server in the information collection system according to one embodiment of the present invention for “evidence determination by comparison of representative values in a specific period”. 本発明に一実施形態に係る情報収集システムにおいて、アドバイスや治療方法等を推論するための推論モデル生成を説明するブロック図である。FIG. 4 is a block diagram illustrating generation of an inference model for inferring advice, treatment methods, etc. in the information collection system according to one embodiment of the present invention; 本発明に一実施形態に係る情報収集システムにおいて、胃・食道の病気の危険因子と、ストレスや生活習慣との関係を示す図である。FIG. 2 is a diagram showing the relationship between risk factors for stomach/esophageal diseases and stress and lifestyle habits in the information collection system according to one embodiment of the present invention.
　以下、本発明の一実施形態として情報処理システムに適用した例について説明する。近年、スマートフォン等の携帯端末は、単独でまたはスマートウォッチ等と連携することによって、この携帯端末の使用者の種々の情報、例えば、心拍数、血圧値、体温等の生体情報や、就寝・起床時間（睡眠時間）、食事内容、購入物品のリスト、運動等の生活習慣情報等を収集することが可能となってきている。 An example applied to an information processing system as an embodiment of the present invention will be described below. In recent years, mobile terminals such as smartphones have been used independently or in cooperation with smart watches to provide various information of the user of the mobile terminal, such as biological information such as heart rate, blood pressure, and body temperature, as well as sleep/wake-up information. It has become possible to collect time (sleep hours), meal contents, a list of purchased items, lifestyle information such as exercise, and the like.
　医師等の診察の際にあたっては、医師等は患者に問診を行い、また検査を行うことによって、現状の症状を把握し、この症状に基づいて病気を治すための処方を行う。しかし、この場合、症状が発生する前と、症状が発生している現在における、生体情報や生活習慣が分かれば、何が原因かがはっきりする。そこで、本実施形態においては、携帯端末に記録されている生体情報や生活習慣情報を取得し、症状が発生していない特定期間と、症状が発生している特定期間におけるパターンを比較し、差異が生じていた生体情報や生活習慣情報に基づいて疾患の原因を特定するようにしている。 At the time of examination by a doctor, etc., the doctor, etc. will ask the patient a question and conduct an examination to understand the current symptoms, and based on these symptoms, prescribe a cure for the disease. However, in this case, if biometric information and lifestyle habits before the symptoms occur and at the time the symptoms occur, the cause can be clarified. Therefore, in the present embodiment, biometric information and lifestyle information recorded in the mobile terminal are acquired, and patterns in a specific period in which symptoms do not occur are compared with patterns in a specific period in which symptoms occur. The cause of the disease is identified based on the biological information and lifestyle information that caused the disease.
　本実施形態に係る情報処理システムでは、まず、患者に対する医師の診察（問診）結果を取得し（例えば、図６ＢのＳ２３、Ｓ２５等参照）、この診察（問診）結果に基づいて、関連する生活習慣や生体情報を判定し、この判定された生活習慣や生体情報に関連した情報を取得するために、患者の携帯端末に記録された情報から必要な情報を選択して要求するようにしている（例えば、図６ＢのＳ２７～Ｓ３１等参照）。携帯端末から取得する情報としては、患者の歩数（加速度）や心拍数の経時変化、また料理の写真等がある。この情報を、他の人の平均と比べた傾向を求めて、患者の疾患を判定するようにしてもよい。 In the information processing system according to the present embodiment, first, the doctor's medical examination (interview) results for the patient are obtained (see, for example, S23 and S25 in FIG. 6B), and based on the medical examination (interview) results, related life Habits and biometric information are determined, and in order to obtain information related to the determined lifestyle habits and biometric information, necessary information is selected and requested from information recorded in the patient's mobile terminal. (See, for example, S27 to S31 in FIG. 6B). The information acquired from the portable terminal includes the patient's number of steps (acceleration), changes in heart rate over time, and photographs of food. This information may be trended against other people's averages to determine the patient's disease.
　図１は、本発明の一実施形態に係る情報収集システムの構成を示すブロック図である。このシステムは、サーバ１０と、携帯端末２０と、サーバ３０を有する。 FIG. 1 is a block diagram showing the configuration of an information collection system according to one embodiment of the present invention. This system has a server 10 , a mobile terminal 20 and a server 30 .
　携帯端末２０は、ユーザが日常生活において携帯して使用するスマートフォン等の端末であり、通信機能を備えている。携帯端末２０は、制御部２１、通信部２２、入力部２３、表示部２４、時計部２５、生体情報取得部２６、生活情報取得部２７、アドバイス部２８、記録部２９を有する。 The mobile terminal 20 is a terminal such as a smartphone that a user carries and uses in his/her daily life, and has a communication function. The mobile terminal 20 has a control section 21 , a communication section 22 , an input section 23 , a display section 24 , a clock section 25 , a biological information acquisition section 26 , a living information acquisition section 27 , an advice section 28 and a recording section 29 .
　制御部２１は、携帯端末２０の全体を制御する。制御部２１は、ＣＰＵ等の処理装置、プログラムを記憶したメモリ等を有する１つ又は複数のプロセッサから構成され、プログラムを実行することによって、携帯端末２０内の各部を制御することができる。 The control unit 21 controls the mobile terminal 20 as a whole. The control unit 21 is composed of one or a plurality of processors having a processing device such as a CPU and a memory storing a program, and can control each unit in the mobile terminal 20 by executing the program.
　通信部２２は、通信回路を有し、携帯電話会社を通じた一般的な電話による通話機能を有し、またインターネット通信網を通じてサーバ等に接続することができる。通信として、例えば、サーバ１０からの要求に応じて、生体情報取得部２７が取得した生体情報や、生活情報取得部２７が取得した生活情報を、サーバ１０に通信部２２を通じて送信することができる。また、通信部２２は、サーバ１０から生体情報比較結果や、ストレス判定結果や、生活習慣判定結果を受信することができる。 The communication unit 22 has a communication circuit, has a call function with a general telephone through a mobile phone company, and can be connected to a server or the like through an Internet communication network. As communication, for example, biometric information acquired by the biometric information acquiring unit 27 and living information acquired by the living information acquiring unit 27 can be transmitted to the server 10 through the communication unit 22 in response to a request from the server 10. . In addition, the communication unit 22 can receive biological information comparison results, stress determination results, and lifestyle habit determination results from the server 10 .
　入力部２３は、表示パネル上のタッチセンサや、その他の操作部材（例えば、操作釦等）と、入力回路等を有する入力インターフェースである。ユーザは、入力部２３を通じて、各種情報等を携帯端末２０に入力することができる。また、入力部２３は、撮像部を有し、画像を入力することもできる。 The input unit 23 is an input interface having a touch sensor on the display panel, other operation members (for example, operation buttons, etc.), an input circuit, and the like. The user can input various information and the like to the mobile terminal 20 through the input unit 23 . The input unit 23 also has an imaging unit and can input an image.
　表示部２４は、表示パネルを有し、例えば、メニュー画面、各種情報、入力画像等の表示を行うことができる。時計部２５は、カレンダー機能を有し、日付けや時刻等の計測を行うことができる。 The display unit 24 has a display panel, and can display, for example, menu screens, various information, input images, and the like. The clock unit 25 has a calendar function and can measure the date, time, and the like.
　生体情報取得部２６は、携帯端末２０を保持するユーザの生体情報を取得する。生体情報取得部２６は、スマートウォッチ等のウエラブル機器と連携して生体情報を取得してもよい。この連携にあたっては、有線および／または無線通信で適宜行えばよい。生体情報としては、例えば、血圧、脈拍、体温等の情報がある。さらに、スマートトイレ等の尿センサ付きの機器と連携できれば、尿等の生体情報を取得することができる。また、携帯端末２０が、体重計、血圧計、体温計、血糖測定器、歩数計等の健康測定器具と連携し、測定値を取得しても良い。健康測定器具等と連携するために、無線ＬＡＮおよび／またはブルートゥース（登録商標）（bluetooth）などの通信部および／または通信制御部を生体情報取得部２６内に設けてもよいし、通信部２２と連携して同様の機能を実現してもよい。 The biometric information acquisition unit 26 acquires biometric information of the user holding the mobile terminal 20 . The biometric information acquisition unit 26 may acquire biometric information in cooperation with a wearable device such as a smartwatch. This cooperation may be appropriately performed by wired and/or wireless communication. Examples of biological information include blood pressure, pulse, body temperature, and the like. Furthermore, biometric information such as urine can be obtained by linking with a device with a urine sensor such as a smart toilet. In addition, the mobile terminal 20 may cooperate with health measuring instruments such as a weight scale, blood pressure gauge, thermometer, blood glucose meter, and pedometer to obtain measured values. In order to cooperate with health measurement equipment, etc., a communication unit such as wireless LAN and/or Bluetooth (registered trademark) (bluetooth) and/or a communication control unit may be provided in the biological information acquisition unit 26, or the communication unit 22 You may realize the same function in cooperation with .
　生活情報取得部２７は、携帯端末２０を保持するユーザの生活情報を取得する。携帯端末２０はＧＰＳ等の衛星測位システムを有している場合には、これを利用してユーザの位置情報を取得することができる。また、携帯端末２０がジャイロセンサや加速度センサ等の動きセンサを有している場合には、これを利用してユーザの動き情報を取得することができる。ユーザの位置や動きが分かれば、ユーザがジョギング等の運動状態にあるとか、就寝状態にあるとか、オフィスで仕事をしている等、種々の生活情報を得ることができる。また、電子マネーの普及によって、携帯端末には店舗毎に支払った結果などが記録される。この購入品明細等の記録は、生活情報となり、また、支払いの場所や時間等に基づいて、ユーザの人の生活のパターン等を判定することが可能になる。 The life information acquisition unit 27 acquires the life information of the user holding the mobile terminal 20 . If the mobile terminal 20 has a satellite positioning system such as GPS, it can use this to acquire the user's location information. Also, if the mobile terminal 20 has a motion sensor such as a gyro sensor or an acceleration sensor, it can be used to acquire user motion information. If the position and movement of the user are known, it is possible to obtain various life information such as whether the user is exercising such as jogging, sleeping, or working in the office. In addition, with the spread of electronic money, mobile terminals record payment results and the like for each store. The records of purchase details and the like serve as living information, and it becomes possible to determine the user's life patterns and the like based on the place and time of payment.
　また、スマートハウス、スマート家電等が普及してきており、携帯端末において、エアコンや照明等を制御できるようになってきている。このため、冷蔵庫にストックした料理した調理した食事メニュー、やその摂取量や排せつや入浴の情報なども、携帯端末で把握できるようになっている。生活情報取得部２７は、スマートハウス、スマート家電等と通信を行い、ユーザの生活習慣情報を取得することができる。 In addition, smart houses, smart home appliances, etc. are becoming popular, and it is becoming possible to control air conditioners, lighting, etc. with mobile terminals. For this reason, it is possible to grasp the menu of cooked meals stocked in the refrigerator, the amount of food intake, excretion, bathing, etc. on the mobile terminal. The lifestyle information acquisition unit 27 can communicate with smart houses, smart home appliances, and the like to acquire lifestyle habit information of the user.
　また、携帯端末２０を使用するユーザは、インターネット等の通信網を通じて、商品やサービスを購入することがあり、これらの情報はサーバ３０等に蓄積されている。また、携帯端末２０を使用するユーザは、日常の活動を示す情報（画像を含む）をＳＮＳ等によって外部のサーバ３０等にアップロードすることがある。そこで、生活情報取得部２７は、サーバ３０等から外部に蓄積されている生活情報を取得するようにしてもよい。 In addition, the user using the mobile terminal 20 may purchase goods and services through a communication network such as the Internet, and this information is stored in the server 30 or the like. Also, the user using the portable terminal 20 may upload information (including images) indicating daily activities to the external server 30 or the like via SNS or the like. Therefore, the life information acquiring unit 27 may acquire life information stored outside from the server 30 or the like.
　アドバイス部２８は、サーバ１０から送信されてくる種々の情報に基づいて、ユーザに健康上の種々のアドバイスを生成する。この生成されたアドバイスは、表示部２４において表示される。 The advice unit 28 generates various health advices for the user based on various information transmitted from the server 10. This generated advice is displayed on the display unit 24 .
　近年では、健康意識の高まりによって、ウエアラブル型の健康管理端末や、通信機能付き体重計や体脂肪計の測定データをスマートフォン（携帯端末）によって管理したり、また測定データを専用のサイトやアプリ（健康診断アプリや生活改善アプリなど）、あるいはそれらの協働したサービスに送信して健康アドバイスを受けたりしているユーザは多い。携帯端末２０は、単に、端末にある情報を利用するだけではなく、このようなサービスと連携するようにしてもよい。この場合、アドバイス部２８は、上述の機能を持たせてもよく、外部のサービスと連携して得た結果を表示してもよい。 In recent years, due to the heightened awareness of health, the measurement data of wearable health management terminals, weighing scales with communication functions, and body fat scales can be managed on smartphones (mobile terminals), and measurement data can be stored on dedicated websites and apps ( health check-up apps, life improvement apps, etc.) or services that collaborate with them to receive health advice. The mobile terminal 20 may not only use the information in the terminal, but may also cooperate with such services. In this case, the advice section 28 may have the functions described above, or may display results obtained in cooperation with an external service.
　記録部２９は、電気的に書き換え可能な不揮発性メモリを有し、生体情報取得部２６、生活情報取得部２７等において取得した情報を記録する。また、入力部２３が取得した画像等の情報があれば、これらを記録してもよい。記録部２９は、患者の現在の病状情報に対して、第１の生体情報パターンと、第２の生体情報パターンを関連づけて記録する記録部として機能する。 The recording unit 29 has an electrically rewritable non-volatile memory, and records information acquired by the biometric information acquisition unit 26, life information acquisition unit 27, and the like. Also, if there is information such as an image acquired by the input unit 23, it may be recorded. The recording unit 29 functions as a recording unit that associates and records the first biological information pattern and the second biological information pattern with respect to the patient's current medical condition information.
　サーバ１０は、ネットワーク上のコンピュータの中で、他のコンピュータ（クライアント）から要求や指示を受け、情報や処理結果を返す役割を持つコンピュータやソフトウエアである。サーバ１０は、前述した携帯端末２０等と通信部１２および通信部２２を通じて、携帯端末２０とデータ等の送受信を行うことができる。このサーバ１０は、病院等の医療施設内のコンピュータと連携して、ユーザの診断・治療や健康管理等を行うことを想定している。医師等の携帯端末やＰＣ（パーソナルコンピュータ）は、サーバ１０に接続可能であり、サーバ１０と接続して日々の診療の際に利用する。なお、サーバ１０は、医療施設等の外部に配置され、医療施設等内の機器と通信可能に接続するようにしてもよい。 The server 10 is a computer or software that has the role of receiving requests and instructions from other computers (clients) and returning information and processing results among the computers on the network. The server 10 can transmit and receive data and the like to and from the portable terminal 20 through the communication unit 12 and the communication unit 22 with the portable terminal 20 and the like. It is assumed that this server 10 cooperates with a computer in a medical facility such as a hospital to diagnose, treat, and manage the health of the user. Portable terminals of doctors and the like and PCs (personal computers) can be connected to the server 10, and are used in daily medical care by connecting to the server 10. FIG. Note that the server 10 may be arranged outside the medical facility or the like, and may be communicably connected to devices inside the medical facility or the like.
　サーバ１０は、制御部１１、通信部１２、問診入力部１３、ＤＢ検索部１４、時計部１５、生体情報比較部１６、ストレス判定部１７、生活習慣判定部１８、および記録・ＤＢ部１９を有する。 The server 10 includes a control unit 11, a communication unit 12, an inquiry input unit 13, a DB search unit 14, a clock unit 15, a biological information comparison unit 16, a stress determination unit 17, a lifestyle determination unit 18, and a recording/DB unit 19. have.
　制御部１１は、サーバ１０の全体を制御する。制御部１１は、ＣＰＵ等の処理装置、プログラムを記憶したメモリ等を有する１つ又は複数のプロセッサから構成され、プログラムを実行することによって、サーバ１０内の各部を制御することができる。 The control unit 11 controls the server 10 as a whole. The control unit 11 is composed of one or a plurality of processors having a processing device such as a CPU and a memory storing a program, and can control each unit in the server 10 by executing the program.
　通信部１２は、通信回路を有し、インターネット等のネットワークを通じて携帯端末２０や他のサーバ等に接続することができる。通信として、例えば、携帯端末２０内の生体情報取得部２７が取得した生体情報や、生活情報取得部２７が取得した生活情報の送信を、携帯端末２０に要求し、これらの情報を通信部１２を通じて受信することができる。また、通信部１２は、サーバ１０において行った生体情報比較結果や、ストレス判定結果や、生活習慣判定結果を携帯端末２０に送信することができる。 The communication unit 12 has a communication circuit and can be connected to the mobile terminal 20 and other servers through a network such as the Internet. As communication, for example, the mobile terminal 20 is requested to transmit the biometric information acquired by the biometric information acquisition unit 27 in the mobile terminal 20 and the life information acquired by the life information acquisition unit 27, and this information is sent to the communication unit 12. can be received through Further, the communication unit 12 can transmit the biological information comparison result, the stress determination result, and the lifestyle determination result performed in the server 10 to the portable terminal 20 .
　通信部１２は、第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンを取得可能な取得部として機能する（例えば、図６ＢのＳ２９参照）。通信部１２は、第１および第２の生体情報パターンの取得に先立って、患者の有する携帯端末に対して第１および第２の生体情報パターンを取得する旨を通知する通信部として機能する（例えば、図６ＢのＳ２９参照）。通信部１２は、原因候補を検証するためのエビデンスデータの送信を患者の携帯端末に要求する通信部として機能する（例えば、図６ＢのＳ２９参照）。この通信部は、第１のパターンと第２のパターンの比較結果に基づいて、原因候補の中から上記患者の疾患の原因が決定されると、患者に対する処方を携帯端末に送信する（例えば、図６ＢのＳ３５参照）。 The communication unit 12 functions as an acquisition unit capable of acquiring a first biological information pattern in a specific period that goes back a first time and a second biological information pattern in a specific period that goes back a second time (for example, S29 reference). Prior to acquiring the first and second biological information patterns, the communication unit 12 functions as a communication unit that notifies the portable terminal of the patient that the first and second biological information patterns are to be acquired ( For example, see S29 in FIG. 6B). The communication unit 12 functions as a communication unit that requests the patient's portable terminal to transmit evidence data for verifying the cause candidate (for example, see S29 in FIG. 6B). When the cause of the patient's disease is determined from the cause candidates based on the result of comparing the first pattern and the second pattern, the communication unit transmits a prescription for the patient to the mobile terminal (for example, See S35 in FIG. 6B).
　第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンを取得できれば、これらの遡り時間で得られた生体情報パターンの差異に基づいて、何か、生活情報および／または生体情報に変化があったかを比較することが可能になり、判定しやすくなる。また、生活情報および／または生体情報は、例えば、定期健康診断が１年おきに開催されたりするように、遡り時間があまり短い時間では、意識して指摘できるような差異が分からない場合があるし、また、２年も３年も経過すると、当時のデータが残っていなかったりする場合がある。また遡り時間が１年の場合には、季節の要因も揃えて比較できるので、１年前の情報と比較することは理に適っている。健康診断は、病気によっては、症状が進行しすぎてしまい、発病の予防や早期の治療に活かせないので１年おきの場合が多い。そこで、第１および第２の生体情報パターンの比較にあたって、この健康診断のデータなどと合わせて行ってもよい。 If a first biological information pattern in a specific period going back a first time and a second biological information pattern in a specific period going back a second time can be obtained, based on the difference between the biological information patterns obtained in these going back times , there has been a change in lifestyle information and/or biometric information, making it easier to determine. In addition, with respect to living information and/or biological information, if the retroactive time is too short, such as when regular health checkups are held every other year, there are cases where differences that can be consciously pointed out may not be apparent. However, after two or three years have passed, there are cases where the data at that time no longer remains. Also, if the retroactive time is one year, seasonal factors can be included in the comparison, so it makes sense to compare the information from one year ago. In many cases, physical examinations are performed every other year because, depending on the disease, the symptoms have progressed too much and cannot be used for prevention of disease onset or early treatment. Therefore, the comparison of the first and second biometric information patterns may be performed together with the health checkup data.
　また、第１および第２の生体情報パターンの比較にあたって、遡り時間の期間についてあまり期間を開けすぎると、加齢の影響や、仕事や家族構成や住居など環境も変わってしまう場合もあり、この場合にはストレスのような繊細なデータの正しい比較が困難となる。理想的には病気になる原因が発生する前のデータと、何らかの原因が発生している時のデータが比較できればよい。また、原因が発生し、発病するまでの期間は、その病気やその原因ストレスの大きさや発生するパターンや患者のストレス耐性などによっても変わるので、「第１の時間」を厳密に規定することは困難である。しかし、これらの要因を分析して、適当な時間を仮定することは可能である。「第１の時間」から遡る「第２の時間」は、上述の問題に加え、この基準の「第１の時間」の変動要因もあって厳密に規定することは困難であるが、同様に要因を分析して適当な時間を仮定してもよい。また、遡り時間の差異として、最もストレスに差異が現れる時間差としてもよい。また、それは後で取捨選択するシステムとして、これらの期間を含むデータ群としてもよい。また、医師の経験や知見によって、適当なタイミングを指定できるようにしてもよい。経験の浅い医師用に、予め決められた時間差などにしてもよい。 Also, when comparing the first and second biometric information patterns, if the period of retroactive time is too long, the influence of aging and the environment such as work, family structure, and residence may change. In some cases, accurate comparison of sensitive data such as stress becomes difficult. Ideally, the data before the cause of the illness should be compared with the data when some cause has occurred. In addition, the period from the occurrence of the cause to the onset of the disease varies depending on the disease, the magnitude of the stress causing it, the pattern of occurrence, and the stress tolerance of the patient. Have difficulty. However, it is possible to analyze these factors and postulate a suitable time. It is difficult to strictly define the "second time" preceding the "first time" due to the above-mentioned problems and the fluctuation factors of the "first time" of this standard, but similarly A suitable time may be assumed by analyzing factors. Alternatively, the difference in retroactive time may be the time difference at which the difference in stress appears most. It may also be a data group containing these periods as a system for selection later. Also, it may be possible to specify an appropriate timing based on the doctor's experience and knowledge. For inexperienced physicians, pre-determined staggers or the like may be used.
　問診入力部１３は、医師等が患者（ユーザ）に問診した際の情報を入力する（例えば、図６ＢのＳ２３参照）。ここでは、医師等が患者に問診したときに手動操作でテキストデータを入力するようにしても良く、また音声データを記録してもよく、また音声を自動的にテキストデータに変換して入力してもよい。 The inquiry input unit 13 inputs information when a doctor or the like interviews a patient (user) (for example, see S23 in FIG. 6B). Here, when a doctor or the like asks a question to a patient, text data may be input manually, voice data may be recorded, or voice data may be automatically converted into text data and input. may
　ＤＢ検索部１４は、記録・ＤＢ１９に記録されているデータベース等を検索することができる。ＤＢ検索部１４は、医師の問診または診断に従って、疾患の原因となる候補を検索する検索部として機能する（例えば、図６ＢのＳ２７、図１０のＳ４１等参照）。 The DB search unit 14 can search databases, etc. recorded in the record/DB 19. The DB search unit 14 functions as a search unit that searches for a candidate that causes a disease according to an inquiry or diagnosis by a doctor (see, for example, S27 in FIG. 6B, S41 in FIG. 10, etc.).
　時計部１５は、カレンダー機能を有し、日付けや時刻等の計測を行うことができる。通常、時間合わせ用のサーバを通じて、日付け・時刻を同期させているので、携帯端末２０およびサーバ１０における時計部の日時情報は一致している。後述するサーバ３０においても同様である。 The clock unit 15 has a calendar function and can measure the date and time. Normally, the date and time are synchronized through a server for time adjustment, so the date and time information of the clock units of the mobile terminal 20 and the server 10 are the same. The same applies to the server 30, which will be described later.
　生体情報比較部１６は、携帯端末２０の生体情報取得部２６において取得された生体情報を比較する。ユーザの生体情報は記録部２９に過去に遡って記録されており、生体情報比較部１６は、過去のある一定期間と現在付近の一定期間の生体情報を比較する。比較することによって、ユーザの生体情報に変化が生じた時期が分かる。後述するように、医師等が診察する際に、過去と現在の生体情報等を比較することによって、症状（疾患）の原因を突き止めることができ、この原因を解消するように処方する。 The biometric information comparison unit 16 compares the biometric information acquired by the biometric information acquisition unit 26 of the mobile terminal 20 . The user's biometric information is retroactively recorded in the recording unit 29, and the biometric information comparison unit 16 compares the biometric information of a certain period of time in the past with that of a certain period of time around the present. By comparing, it is possible to know when the user's biometric information has changed. As will be described later, a doctor or the like can identify the cause of a symptom (disease) by comparing past and present biological information, etc., at the time of examination, and prescribe to eliminate the cause.
　生体情報比較部１６は、特定期間において、第１および第２の生体情報パターンの代表値を比較することによって行う原因エビデンス判定は、候補となる原因毎に上記遡った時期における上記第１および第２の生体情報パターンの差異を調べる比較部として機能する。比較部は、原因エビデンス判定において、遡る時期は、候補となる原因の差異を調べ、差異が明確な時期とする。また、比較部は、医師の問診または診断に従って選択された候補となる原因に基づいて、第１および第２の生体情報パターンの種類を選択し、この選択された種類の上記第１及び第２の生体情報パターンについて差異を調べる（例えば、図５ＢのＳ２７、図１０のＳ４１等参照）。生体情報比較部１６は、携帯端末からエビデンスデータを受信すると、このエビデンスデータについての、第１の時間遡る特定期間における第１のパターンと、第２の時間遡る特定期間における第２のパターンを比較する比較部として機能する（例えば、図６ＢのＳ３１、図８のグラフＧｒ５、Ｇｒ６等参照）。 The biometric information comparison unit 16 performs cause evidence determination by comparing the representative values of the first and second biometric information patterns in the specific period, and determines the cause evidence based on the first and the first in the retroactive period for each candidate cause. It functions as a comparison unit that examines the difference between the two biometric information patterns. In the causal evidence determination, the comparison unit examines the differences in the candidate causes as the retroactive time, and determines the time when the differences are clear. Further, the comparison unit selects the types of the first and second biological information patterns based on the candidate cause selected according to the doctor's inquiry or diagnosis, and selects the selected types of the first and second biological information patterns. (See, for example, S27 in FIG. 5B, S41 in FIG. 10, etc.). When receiving the evidence data from the portable terminal, the biological information comparison unit 16 compares the first pattern in the specific period going back in time with the second pattern in the specific period going back in time for the second time. (See, for example, S31 in FIG. 6B, graphs Gr5 and Gr6 in FIG. 8, etc.).
　ストレス判定部１７は、生体情報等に基づいて、ユーザがストレスを感じているか否かについて判定する。ユーザがストレスを感じている場合には、血圧（脈拍）や睡眠時間等に変化が現れるので、これらの生体情報の変化に基づいて、ストレス判定部１７が判定を行う。ユーザのストレスの詳細については、図４Ａないし図５を用いて後述する。ストレス判定部１７は、第１の生体情報パターンと第２の生体情報パターンを比較し、この比較に基づいて、患者のストレスを判定するストレス判定部として機能する（例えば、図６ＢのＳ３１参照）。 The stress determination unit 17 determines whether or not the user is feeling stress based on biological information and the like. When the user feels stress, changes appear in blood pressure (pulse), sleep time, etc. Therefore, the stress determination unit 17 performs determination based on these changes in biological information. The details of the user's stress will be described later with reference to FIGS. 4A to 5. FIG. The stress determination unit 17 compares the first biological information pattern and the second biological information pattern, and functions as a stress determination unit that determines the patient's stress based on this comparison (see S31 in FIG. 6B, for example). .
　生活習慣判定部１８は、携帯端末２０の生活情報取得部２６によって取得されたユーザの生活情報に基づいて、生活習慣を判定する。図１２を用いて後述するように、生活習慣は疾患の原因となり、逆に病気を患うような場合には、その原因となる生活習慣がある場合がある。そこで、本実施形態においては、生活習慣判定部１８は、携帯端末２０の生活情報取得部２６によって取得した生活情報に基づいて、ユーザがどのような生活習慣を有しているか判定し、この生活習慣に基づいて、ユーザが健康的に過ごせるようなアドバイスを行うための推論モデルを生成している（例えば、図６ＡのＳ３、Ｓ５、図６ＢのＳ３５、図１１等参照）。 The lifestyle determination unit 18 determines lifestyle habits based on the user's lifestyle information acquired by the lifestyle information acquisition unit 26 of the mobile terminal 20 . As will be described later with reference to FIG. 12, lifestyle habits cause diseases, and conversely, when a person suffers from a disease, there may be lifestyle habits that cause the disease. Therefore, in the present embodiment, the lifestyle determination unit 18 determines what kind of lifestyle the user has based on the lifestyle information acquired by the lifestyle information acquisition unit 26 of the mobile terminal 20, Based on habits, an inference model is generated for giving advice for the user to live a healthy life (see, for example, S3 and S5 in FIG. 6A, S35 in FIG. 6B, and FIG. 11).
　記録・ＤＢ部１９は、電気的に書き換え可能な不揮発性メモリであり、サーバ１０が取得した種々の情報を記録する。記録・ＤＢ部１９は、プロフィール毎の診察記録部１９ｂ、プロフィール毎の生体情報１９ｃ、および疾病別原因１９ｄを記録している。前述したＤＢ検索部１４は、この記録・ＤＢ部１９に記録されている情報を検索できる。診察記録部１９ｂは、患者毎に診察した際のカルテ等の記録である。この診察記録部１９ｂには、例えば、患者の基礎情報、問診時等の情報（電子カルテ情報等を含む）が記録されており、また、携帯端末２０から取得したユーザの基本的な情報も合わせて記録されている。 The recording/DB unit 19 is an electrically rewritable non-volatile memory, and records various information acquired by the server 10 . The recording/database unit 19 records a medical examination recording unit 19b for each profile, biometric information 19c for each profile, and disease-specific causes 19d. The DB search unit 14 described above can search information recorded in the recording/DB unit 19 . The medical examination recording section 19b is a record of medical charts and the like when each patient is examined. In this medical examination recording unit 19b, for example, the patient's basic information and information at the time of medical interview (including electronic medical record information, etc.) are recorded. is recorded.
　プロフィール毎の生体情報１９ｃには、携帯端末２０の生体情報取得部２６によって取得された生体情報が記録されている。生活情報取得部２７によって取得された生活習慣情報等が記録されていてもよい。また、疾病別原因１９ｄには、図７に示すような、症状と、その原因候補と、原因を裏付けるエビデンスデータと、このエビデンスデータを取得するための機器が記録されている。 The biometric information acquired by the biometric information acquisition unit 26 of the mobile terminal 20 is recorded in the biometric information 19c for each profile. Lifestyle information and the like acquired by the lifestyle information acquisition unit 27 may be recorded. Further, in the cause by disease 19d, as shown in FIG. 7, symptoms, candidate causes thereof, evidence data supporting the causes, and devices for obtaining the evidence data are recorded.
　サーバ３０は、ネットワーク上のコンピュータの中で、他のコンピュータ（クライアント）から要求や指示を受け、情報や処理結果を返す役割を持つコンピュータやソフトウエアである。前述のサーバ１０は、病院等の医療施設内のコンピュータと連携して、ユーザの診断・治療や健康管理等を行うことを想定していたが、このサーバ３０は、一般的な多目的のサーバである。サーバ３０内の通信部は、携帯端末２０やサーバ１０とも通信可能であり、また他の携帯端末やサーバとも通信可能である。このため、このサーバ３０は、一般的な情報が記録されている。 The server 30 is a computer or software that has the role of receiving requests and instructions from other computers (clients) on the network and returning information and processing results. The above-described server 10 was assumed to cooperate with computers in medical facilities such as hospitals to perform diagnosis, treatment, health management, etc. of users, but this server 30 is a general multi-purpose server. be. The communication unit in the server 30 can communicate with the mobile terminal 20 and the server 10, and can also communicate with other mobile terminals and servers. Therefore, general information is recorded in this server 30 .
　本実施形態においては、疾患の原因となったかもしれないデータは、ストレス値を用いて説明した（例えば、ストレス判定部１７参照。なお、図２ないし図５を用いて更に詳述する）。しかし、疾患の原因となったかもしれないデータは、ストレス値に限らず、食事や睡眠、運動不足などを含めた生活の乱れや不摂生などがありうる。暴飲暴食、カロリー摂取過多等も想定して良い。これらの疾患の原因となったかもしれないデータが何であったかは、先の健康診断アプリや生活改善アプリや、アプリ－サーバ連携サービスにこうした機能を追加依頼したものからの判定結果を使用してもよく、生体情報比較部１６と連携して判定してもよく、アドバイス部２８が、これを判定してもよい。 In this embodiment, the data that may have caused the disease are explained using the stress value (for example, see the stress determination unit 17. Further details will be given with reference to FIGS. 2 to 5). However, the data that may have caused the disease are not limited to the stress value, but may include disordered lifestyles and unhealthy lifestyles, including lack of diet, sleep, and exercise. Binge eating and drinking, excessive calorie intake, etc. may also be assumed. The data that may have caused these diseases cannot be determined even by using the judgment results from the health checkup application, the life improvement application, and the request for adding such functions to the application-server cooperation service. Well, the determination may be made in cooperation with the biological information comparing section 16, or the advice section 28 may make the determination.
　なお、先の健康診断アプリや生活改善アプリや、アプリ－サーバ連携サービスが、医師からの要望で、関連情報をすぐに送信できるように、患者用ユーザーインターフェースの他に、患者を診断する医師用のユーザーインターフェースを持つようにしてもよい。つまり、この健康診断アプリや生活改善アプリや、アプリ－サーバ連携サービスは、医師からの依頼を受ける依頼取得ステップと、医師からの依頼に応じて、患者の健康関連情報（ここでは、生体情報のみならず、それを加工したり解釈したりしたものを含むことを想定してもよい）の医師への送信を許可するかどうかの記載を伴うスイッチ表示を行うスイッチ表示ステップと、スイッチ表示に先立って、記録されていた情報から、少なくとも第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンと、を含む情報を送信する送信ステップを有する。こうしたステップを具備する情報共有方法を本実施形態は含んでいる。もちろん端末が、第１の生体情報パターンと第２の生体情報パターンを比較し、この比較に基づいた情報を作って送信してもよく、さらには、そこから解析した患者のストレスを判定するストレス判定ステップを有し、その判定結果を送信するようにしてもよい。 In addition to the user interface for patients, we have added a user interface for doctors diagnosing patients so that the health checkup app, life improvement app, and app-server linkage service can immediately send related information at the request of doctors. may have a user interface of In other words, this health checkup application, life improvement application, and application-server cooperation service include a request acquisition step for receiving a request from a doctor, and a patient's health-related information (here, biometric information only) in response to a request from a doctor. A switch display step of performing a switch display with a description as to whether or not to permit transmission to the doctor, and a switch display step prior to the switch display a transmitting step of transmitting, from the recorded information, information including at least a first biological information pattern for a specific period going back at least a first time period and a second biological information pattern for a specific period going back a second time period; have This embodiment includes an information sharing method comprising these steps. Of course, the terminal may compare the first biometric information pattern and the second biometric information pattern, create and transmit information based on this comparison, and further determine the patient's stress analyzed therefrom. It may have a determination step and transmit the determination result.
　次に、図２および図３を用いて、本発明の一実施形態に係る情報収集システムの使用例について説明する。図２は、医師５３が患者５１を診察している様子を示す。診察にあたって、医師５３は、患者５１に色々質問を行い、すなわち問診を行い、患者５１は、医師５３の質問に答えている。 Next, a usage example of the information collection system according to one embodiment of the present invention will be described using FIGS. 2 and 3. FIG. FIG. 2 shows a doctor 53 examining a patient 51 . During the medical examination, the doctor 53 asks the patient 51 various questions, that is, interviews the patient 51, and the patient 51 answers the doctor's 53 questions.
　図２に示す例では、例えば、医師５３が症状の原因を探るために、患者５１の生活情報が必要と考え、「生活情報を貰えますか？」と依頼すると、患者５１は「分かりました。」と答え、携帯端末２０を用いて、患者５１の生活情報を送信する。このようなやり取りは、個人情報に関わるので、患者や医師の双方の正しい理解のもとに行われた方が良い。そこで、口頭でやり取りするだけではなく、病院の受付や、端末上の電子的な手続きで、条件や免責事項などを確認の上、行ってもよい。また、システム的にこの問題を対策し、「医師から求められる可能性があります」といった説明を、同様に別途、患者に行っておくようにしてもよい。 In the example shown in FIG. 2, for example, when the doctor 53 thinks that the living information of the patient 51 is necessary in order to investigate the cause of the symptoms, and asks, "Could you give me the living information?" ”, and uses the portable terminal 20 to transmit the living information of the patient 51 . Since such exchanges involve personal information, it is better to be conducted under the correct understanding of both the patient and the doctor. Therefore, in addition to verbal communication, conditions and disclaimers may be confirmed at the reception of the hospital or electronically on a terminal. In addition, this problem may be systematically dealt with and an explanation such as "There is a possibility that this may be requested by the doctor" may be separately sent to the patient.
　送信にあたって、図３に示すように、患者５１の端末携帯２０の表示部２４の表示画面２４ａに、「この情報を医師に送りますか？」が表示される。仕様によっては、医師に送る情報を最初に取り決めしておく方法もあるが、このような表示で、送信するものを取捨選択できるようにしてもよい。「送りますか？」に対して、表示部２４の表示画面に「Ｙｅｓ」、「Ｎｏ」を表示し、以下に表示された項目のデータを送信するようにしてもよい。表示画面２４ｂには「最近の生活情報データ」の具体的数値が表示される表示部があり、表示画面２４ｃには「１年前生活情報データ」の具体的数値が表示される表示部がある。これらの表示部のうち、タッチした方を送るようにしてもよい。タッチすれば、そのデータは消えて送れないようにする表示方法もある。また、音声操作などで、取捨選択や指示をできるようにしてもよい。生活習慣や生体情報の履歴なので、実際にはもっとたくさんの候補が表示される可能性もあるが、項目のみならず、データ取得した日時等の情報も表示して、ユーザが、どのデータであるかを把握し易くしてもよい。 When sending, as shown in FIG. 3, "Would you like to send this information to the doctor?" Depending on the specifications, there is a method in which the information to be sent to the doctor is arranged in advance, but it is also possible to select the information to be sent in such a display. In response to "Do you want to send?", "Yes" and "No" may be displayed on the display screen of the display unit 24, and the data of the items displayed below may be transmitted. The display screen 24b has a display section for displaying specific numerical values of "recent living information data", and the display screen 24c has a display section for displaying specific numerical values of "one year ago living information data". . Of these display sections, the one that is touched may be sent. There is also a display method that, when touched, erases the data and prevents it from being sent. In addition, selection and instruction may be performed by voice operation or the like. Since it is a history of lifestyle habits and biometric information, there is a possibility that more candidates will actually be displayed. You may make it easy to grasp whether.
　また、実際には、何年何月何日何時何分に取得されたデータであるかまで履歴があるとしても、一年前のデータとか、抽象化したデータにして送信するような工夫を行うことが好ましい。個々のデータではなく、そこから読みとれる情報を抽象化して送信してもよい。例えば、「ハンバーグ購入」を「洋食（肉）」という形にして、送信してもよい。購入した場所なども特に必要な情報でない場合は削除する。また、同様の項目の複数のデータは、まとめやすいくくりで特定の期間にまとめて抽象化する方法もある。例えば、一週間のうち飲酒は２回といった情報にしてもよい。一週間で肉食３回、魚４回というまとめ方でもよい。三日に一回は魚をメインにした食事だった、という情報であれば、抵抗感なく送信することが想定される。抽象化と書いたが、サマリー（要約）のような表現でもよい。サマリーにするかしないかを。選べるようにしてもよいし、サマリーにして、抽象化や匿名化が出来たかどうかを、表示してみて確認できるようにしてもよい。 Also, in practice, even if there is a history of how many years, months, days, hours, and minutes the data was acquired, it should be devised so that it can be sent as abstracted data, such as data from a year ago. is preferred. Instead of individual data, information that can be read from it may be abstracted and transmitted. For example, "buy hamburger" may be changed to "Western food (meat)" and transmitted. If the place of purchase is not particularly necessary information, delete it. In addition, there is also a method of abstracting a plurality of data of similar items together in a specific period in an easy-to-collect manner. For example, information such as drinking twice in a week may be used. It is also possible to summarize eating meat three times and fish four times a week. If it is information that the main meal was fish once every three days, it is assumed that the user will send it without hesitation. I wrote "abstraction", but an expression like "summary" is also acceptable. To summarize or not. It may be possible to select it, or it may be made into a summary so that it can be displayed and confirmed whether abstraction and anonymization have been achieved.
　表示画面２４ａにおいて、患者５１が送信に同意すると、これらの生活データは、サーバ１０を通じて、医師５３のＰＣ端末１０Ａに送信される。ＰＣ端末１０Ａは、サーバ１０と接続されており、患者５１の最近と１年前の生活情報データが、サーバ１０に入力されると共にＰＣ端末１０Ａにも転送される。 When the patient 51 agrees to the transmission on the display screen 24a, these life data are transmitted to the PC terminal 10A of the doctor 53 through the server 10. The PC terminal 10A is connected to the server 10, and the recent and one-year-old living information data of the patient 51 are input to the server 10 and also transferred to the PC terminal 10A.
　患者５１は、図２に示す例では、特にストレスを自覚していないようであるが、医師５３は、現在の症状の原因が何であるかについて、送信されてきた生活情報データ（生体情報であってもよい）を見ながら、何が原因となっていているかを探す。原因が分かると、この原因を解決するために、何を改善したら良いかを、患者５１と一緒に考えることを提案する。 In the example shown in FIG. 2, the patient 51 does not seem to be particularly aware of stress, but the doctor 53 asks what is the cause of the current symptoms based on the transmitted life information data (biological information). ) to find out what is causing it. When the cause is found, it is proposed to think together with the patient 51 what should be improved in order to solve this cause.
　このように、本実施形態においては、ユーザ（患者）携帯端末２０が、ユーザの生活情報や生体情報を予め記録しておき、医師による診察の際に、最近と過去の特定期間における生活情報等を、医師のＰＣ端末と、そのサーバに送信している。サーバ１０は、後述するように、最近と過去の特定期間における生活情報等を比較することによって、症状の原因となった生活習慣等を知ることができる。この特定期間は、疾患の想定される原因等や、患者の体質等を考慮して適宜決めればよい。また、色々期間を試してみて、特徴が明瞭になる期間であってもよい。なお、図２および図３においては、生活習慣についてものみ言及しているが、医師等は生体情報についても患者から取得し、疾患の原因を検討してもよい。 As described above, in the present embodiment, the user (patient) portable terminal 20 pre-records the user's life information and biological information, and the life information and the like in recent and past specific periods are recorded by the doctor when the doctor examines the patient. is transmitted to the doctor's PC terminal and its server. As will be described later, the server 10 can know the lifestyle habits and the like that caused the symptoms by comparing the lifestyle information and the like in recent and past specific periods. This specific period may be appropriately determined in consideration of the possible cause of the disease, the patient's constitution, and the like. Also, it may be a period in which various periods are tried and the characteristics become clear. 2 and 3, only lifestyle habits are mentioned, but a doctor or the like may also obtain biological information from the patient and examine the cause of the disease.
　図２および図３に示す例では、症状の原因となった生活習慣等を探索するようにしている。症状の原因としては、図２の例に示すように、ストレスの場合が多い。そこで、このストレスについて、説明する。 In the examples shown in Figures 2 and 3, the lifestyle habits that caused the symptoms are searched. Symptoms are often caused by stress, as shown in the example of FIG. Therefore, this stress will be explained.
　ストレスがあると、自律神経のバランスは交感神経優位へ偏位し、心血管系の変性が促進され、交感神経緊張の亢進と副交感神経緊張の減少によって、心不全、冠動脈疾患、急性心筋梗塞による死亡率と関連がある他、人体の各部に影響を与えると言われている。ストレスがあると、体内のバランスの乱れによって呼吸器系の障害を引き起こし、免疫力の低下等にも影響し、消化器系に影響を及ぼしたりもする。自律神経系は、血液循環・呼吸・体温調節など、意識の介在なしに制御するシステムで、「交感神経系」と「副交感神経系」があり、「交感神経系」は身体の活動レベルや運動能力を高める方向に働き、「副交感神経系」は心身の鎮静化・エネルギーの消費抑制と蓄えの方向に働く。 Under stress, the balance of the autonomic nervous system shifts in favor of the sympathetic nervous system, promoting degeneration of the cardiovascular system, increasing sympathetic tone and decreasing parasympathetic tone, leading to death from heart failure, coronary artery disease, and acute myocardial infarction. It is said to affect various parts of the human body in addition to being related to the rate of injury. When there is stress, the balance in the body is disturbed, causing respiratory system disorders, lowering immunity, etc., and affecting the digestive system. The autonomic nervous system is a system that controls blood circulation, breathing, temperature regulation, etc. without conscious intervention. It works in the direction of increasing ability, and the "parasympathetic nervous system" works in the direction of calming the mind and body, suppressing consumption of energy, and storing energy.
　心拍変動は交感神経と副交感神経の両方に影響を表すので、心電図のＲＲＩを周波数解析してパワースペクトルにしたときに、ＬＦ（Low Frequency）とＨＦ（High Frequency）の２つの領域（ＬＦ値は0.05Hz～0.15Hzの値、ＨＦ値は0.15Hz～0.40Hzの値）のバランスを調べることで、その人のストレスを数値化することが出来る。つまり、ＨＦ成分は呼吸によって生ずる副交感神経活動によって影響を受けるとされ、ＬＦ成分は交感神経と副交感神経活動によって影響を受けるとされているからである。この心拍変動の解析方法については、図４Ａおよび図４Ｂを用いて後述する。 Since heart rate variability affects both the sympathetic and parasympathetic nerves, when the RRI of an electrocardiogram is frequency-analyzed and turned into a power spectrum, the two regions of LF (Low Frequency) and HF (High Frequency) (LF value is By examining the balance between 0.05 Hz to 0.15 Hz and 0.15 Hz to 0.40 Hz for HF values, the person's stress can be quantified. That is, the HF component is said to be influenced by parasympathetic nerve activity caused by respiration, and the LF component is said to be influenced by sympathetic and parasympathetic nerve activity. This heart rate variability analysis method will be described later with reference to FIGS. 4A and 4B.
　なお、心拍数の計測には、心電図法、光電脈波法、血圧計測法、心音図法と大きく４つの方法が知られている。上述の光電脈波法の中でも、反射型脈波センサを使う方法は、赤外線や赤色光、 550nm付近の緑色波長の光を生体に向けて照射し、フォトダイオード又はフォトトランジスタを用いて、生体内を反射した光を計測するだけなので、多くのウエアラブル機器で用いられている。動脈の血液内には酸化ヘモグロビンが存在し、入射光を吸収する特性があるため、心臓の脈動に伴って変化する血流量（血管の容量変化）を時系列にセンシングすることで脈波信号を計測できる。血流量センシングは、透過型の脈波センサや、顔色に表れる色の変化の検出によっても判定できることが知られている。 There are four known methods for measuring heart rate: electrocardiography, photoplethysmography, blood pressure measurement, and phonocardiography. Among the photoplethysmographic methods described above, the method using a reflective pulse wave sensor irradiates the living body with infrared light, red light, and light with a green wavelength around 550 nm, and uses a photodiode or phototransistor to detect in vivo It is used in many wearable devices because it only measures the reflected light. Oxygenated hemoglobin is present in arterial blood and has the property of absorbing incident light. Therefore, pulse wave signals can be obtained by sensing changes in blood flow (volume changes in blood vessels) that accompany the pulsation of the heart in chronological order. can be measured. It is known that blood flow sensing can also be determined by a transmissive pulse wave sensor or detection of changes in color appearing on the face.
　また、心臓には洞房結節という部分があり、電気パルスを発生して心筋を収縮させることで周期的に拍動させているので、体の数箇所に電極パッドを付け、心臓から発生する微弱な電気パルスを捉えて心電図を測る方法もある。 In addition, the heart has a part called the sinoatrial node, which generates electrical pulses to contract the heart muscle and cause it to beat periodically. There is also a method of measuring an electrocardiogram by capturing electrical pulses.
　前述した心拍変動の解説について図４Ａおよび図４Ｂを用いて説明する。概略、心拍波形のピーク（Ｒ波）を検出、心拍のピークからピークまでの時間（ＲＲＩ：ＲＲ間隔）を計測すれば、数値化が可能になる。これを線形補間で再サンプリングを行う、フーリエ変換を行い、パワースペクトラムにする、ストレス指標ＬＦ／ＨＦを算出すればよい。これは、先のＬＦ値、ＨＦ値の説明で示した周波数範囲にある振幅値の合計を出して算出したものである。 The above explanation of heart rate variability will be explained using FIGS. 4A and 4B. Generally speaking, if the peak (R wave) of the heartbeat waveform is detected and the time from the peak to the peak of the heartbeat (RRI: RR interval) is measured, quantification becomes possible. This is subjected to re-sampling by linear interpolation, Fourier transform is performed, a power spectrum is obtained, and a stress index LF/HF is calculated. This is calculated by summing the amplitude values in the frequency range shown in the previous explanation of the LF value and HF value.
　心拍変動の解析にあたっては、まず、図４ＡのグラフＧｒ１に示すように脈波を取得し、この取得した脈波を微分して、図４ＡのグラフＧｒ２に示すような速度脈波を取得し、この速度脈波をさらに微分することによって、図４ＡのグラフＧｒ３に示すような加速度脈波を取得する。この加速度脈波は、心電図相当の波形でありＲＲＩの解析をすることが可能である。必要に応じて、フィルタ処理やノイズ除去処理などで波形を整える。 In analyzing the heart rate variability, first, a pulse wave is acquired as shown in the graph Gr1 of FIG. 4A, and the acquired pulse wave is differentiated to acquire a velocity pulse wave as shown in the graph Gr2 of FIG. 4A, By further differentiating this velocity pulse wave, an acceleration pulse wave as shown in graph Gr3 in FIG. 4A is obtained. This accelerated pulse wave is a waveform equivalent to an electrocardiogram and can be analyzed for RRI. If necessary, the waveform is adjusted by filtering, noise removal, etc.
　心電図波形において、最も高いピークをＲ波とよび、ＲＲＩは、Ｒ波と次のＲ波の間隔をいう。図４ＡのグラフＧｒ３に示す加速度脈波は心電図相当であり、この加速度脈波において、波形のピークを検出し、ピークからピークまでの時間（ＲＲＩ）を計測し、線形補間で再サンプリングを行う。図４ＡのグラフＧｒ３の加速度脈波信号に対して、フーリエ変換を行うと、図４Ｂに示すような、パワースペクトラムを得ることができる。このパワースペクトラムにおいて、ストレス指標ＬＦ／ＨＦを算出することができる。 In the electrocardiogram waveform, the highest peak is called the R wave, and RRI is the interval between the R wave and the next R wave. The accelerated pulse wave shown in graph Gr3 of FIG. 4A corresponds to an electrocardiogram. In this accelerated pulse wave, the peak of the waveform is detected, the time from peak to peak (RRI) is measured, and resampling is performed by linear interpolation. A power spectrum as shown in FIG. 4B can be obtained by Fourier transforming the acceleration pulse wave signal of graph Gr3 in FIG. 4A. In this power spectrum, the stress index LF/HF can be calculated.
　図４Ｂに示すパワースペクトラムにおいて、ＬＦ領域は、特定周波数ｆ＿ｔｈより低い周波数の領域である。人がストレスを感じると、ＬＦ領域の面積が増加する。一方、ＨＦ領域は、特定周波数ｆ＿ｔｈより高い周波数の領域である。人がリラックスしていると、ＨＦ領域の面積が増加する。副交感神経が優位にある場合にＨＦ成分が現れるため、ＨＦ成分の数値を副交感神経の活性度（緊張度）とする場合もある。また、交感神経が優位でも、副交感神経が優位でも、ＬＦ成分が現れるため、ＬＦとＨＦの比をとって、ＬＦ／ＨＦをストレス指標（交感神経の活性度）とする。 　In the power spectrum shown in FIG. 4B, the LF region is a region of frequencies lower than the specific frequency f_th. When a person feels stress, the area of the LF region increases. On the other hand, the HF region is a frequency region higher than the specific frequency f_th. When a person is relaxed, the area of the HF region increases. Since the HF component appears when the parasympathetic nerve is dominant, the numerical value of the HF component may be used as the degree of activity (tone) of the parasympathetic nerve. Since the LF component appears whether the sympathetic nerve is dominant or the parasympathetic nerve is dominant, the ratio of LF to HF is taken, and LF/HF is used as a stress index (sympathetic nerve activity).
　リラックスしている状態、つまり副交感神経が活性化しているときには、呼吸変動を反映したＨＦ成分と血圧変動を反映したＬＦ成分も現れる。ストレス状態にある場合、つまり交感神経が活性化しているときには、ＬＦ成分が現れる一方ＨＦ成分が減少する。従って、リラックス状態にあると相対的にＨＦ成分が大きくなるのでＬＦ／ＨＦの値は小さくなり、反対に、ストレス状態にあるとＨＦに対してＬＦ成分が大きくなるのでＬＦ／ＨＦの値が大きくなる。 In a relaxed state, that is, when the parasympathetic nerves are activated, the HF component reflecting breathing fluctuations and the LF component reflecting blood pressure fluctuations also appear. In a stress state, that is, when the sympathetic nerve is activated, the HF component decreases while the LF component appears. Therefore, in the relaxed state, the HF component becomes relatively large, resulting in a small LF/HF value. Become.
　次に、図５を用いて、血圧とストレスの関係について説明する。ストレスによって血圧が上昇することも知られている。この現象も交感神経の活性化などによって引き起こされ、交感神経が活性化すると、心拍数が増加するのに対し、小動脈は収縮することによって、血圧が上昇する。リラックスしている睡眠時は、血圧が下がっているのに対し、起床時は高めになる。図５は、一日における血圧の変化の一例を示すグラフである。このグラフから分かるように、食事や入浴時にピークを示し、ストレスがある時にもピークが見られる。一日の血圧のピーク数やその最大値を利用してストレスを測定することが可能である。 Next, the relationship between blood pressure and stress will be explained using FIG. It is also known that stress raises blood pressure. This phenomenon is also caused by the activation of the sympathetic nerves. When the sympathetic nerves are activated, the heart rate increases, while the small arteries constrict, resulting in an increase in blood pressure. During relaxed sleep, blood pressure is low, whereas it is high when waking up. FIG. 5 is a graph showing an example of changes in blood pressure in one day. As can be seen from this graph, peaks are seen during meals and bathing, and peaks are also seen when there is stress. It is possible to measure stress using the peak number of blood pressure in a day and its maximum value.
　また、心拍数の変化をグラフ化しても、ストレス時には上昇する傾向がみられる。加速度センサなどと組合せ、特に運動をしていない時にも、血圧や心拍数が上がる時には何らかのストレスを疑うことが出来る。 Also, even if you graph changes in heart rate, you can see a tendency to increase during stress. In combination with an acceleration sensor, it is possible to suspect some kind of stress when blood pressure or heart rate rises, even when not exercising.
　血圧の測定にあたっては、腕に空気で膨らむカフを巻き付けるタイプの「オシロメトリック法」で測定してもよい。これ以外にも、全身の循環器系を数学的に表し、心臓をポンプ、血管を弾性のある容器、血管と血管を結ぶ細い血管は抵抗として、流体力学を使い血液の流れをモデル化する「血流動態センシング」によって測定してもよい。この血流動態センシングのモデルを用いて心拍数から血圧を算出することができる。スマートウォッチが採用している心拍数の測定と光学センサによる血流測定を組み合わせて、血圧を算出する方法も知られている。 Blood pressure can be measured by the "oscillometric method", which involves wrapping an inflatable cuff around the arm. In addition to this, the circulatory system of the whole body is represented mathematically, and the blood flow is modeled using fluid dynamics, with the heart as a pump, blood vessels as elastic containers, and thin blood vessels that connect blood vessels as resistance. may be measured by "hemodynamic sensing". Blood pressure can be calculated from heart rate using this hemodynamic sensing model. A method of calculating blood pressure by combining heart rate measurement adopted by a smart watch and blood flow measurement by an optical sensor is also known.
　次に、図６Ａに示すフローチャートを用いて、携帯端末２０における動作を説明する。図６Ａのフローは、携帯端末２０内の制御部２１が、メモリに記憶されたプログラムに従って携帯端末２０内の各部を制御することによって実現する。 Next, the operation of the mobile terminal 20 will be described using the flowchart shown in FIG. 6A. The flow of FIG. 6A is realized by control unit 21 in mobile terminal 20 controlling each unit in mobile terminal 20 according to a program stored in the memory.
　携帯端末２０は、医師の診断の助けとなる、あるいは、疾病予測用の推論モデル作成用の教師データの元になる情報を集めて、外部に提供する装置としての役割も果たしている。また端末内蔵のアプリケーションソフトは、この端末が連携するウエアラブル端末やインターネットサービスと連携して、以下のような働きをする。つまり、この端末の記録部に記録され、端末上で機能する康診断アプリや生活改善アプリや、アプリ－サーバ連携サービスは、医師からの依頼を受ける依頼取得ステップと、医師からの依頼に応じて、端末やそれと連携した装置に記録されていた情報から、少なくとも第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンと、を含む情報を選択するステップと、収集された患者の健康関連情報（ここでは、生体情報のみならず、それを加工したり解釈したりしたものを含むことを想定してもよい）の医師への送信を許可するかどうかの記載を伴うスイッチ表示ステップを有している。本実施形態には、このような情報共有方法を含んでいる。もちろん端末が、第１の生体情報パターンと第２の生体情報パターンを比較し、この比較に基づいた情報を作って送信してもよく、さらには、そこから解析した患者のストレスを判定するストレス判定ステップを有し、その判定結果を送信するようにしてもよい。 The mobile terminal 20 also serves as a device that collects and provides externally information that aids doctors in diagnosing or serves as the source of teacher data for creating an inference model for disease prediction. In addition, the application software built into the terminal performs the following functions in cooperation with the wearable terminal and Internet service with which this terminal cooperates. In other words, the health diagnosis application, life improvement application, and application-server cooperation service that are recorded in the recording unit of this terminal and that function on the terminal have a request acquisition step for receiving a request from a doctor, and a request acquisition step for receiving a request from a doctor. , a first biological information pattern in a specific period going back at least a first time and a second biological information pattern going back a second time from information recorded in a terminal or a device linked thereto, Selecting information and transmitting collected patient health-related information (here, it may be assumed to include not only biometric information but also any processing or interpretation thereof) to the physician. has a switch display step with a description of whether or not to allow This embodiment includes such an information sharing method. Of course, the terminal may compare the first biometric information pattern and the second biometric information pattern, create and transmit information based on this comparison, and further determine the patient's stress analyzed therefrom. It may have a determination step and transmit the determination result.
　以下に、図６Ａの動作を詳細に説明する。携帯端末の動作が開始すると、まず、アンケートやバイタル情報を取得する（Ｓ１）。携帯端末の動作が開始すると、一般にユーザのタッチ操作、スイッチ操作によって電話やメール、インターネットの情報検索などが出来る。また、ユーザが携帯端末を持ち歩くと内蔵された振動センサ等によってユーザの歩行を検出したり、内蔵するＧＰＳや携帯電話の基地局の情報によって行動が記録されたりする。また、内蔵カメラによって、ユーザの表情や顔色の判定などが可能であり、撮影画像は生体情報取得の一助となる。また、ユーザが装着する腕時計型端末と連携している場合には、この腕時計型端末が皮膚に当接しているため、体温を検知することができ、また腕時計型端末の背面に設けられた発光素子が発光し、反射光を受光して、その反射変化パターンから、心拍数や血圧などを判定することが出来る。これらの情報を取得することによって、バイタル情報が取得される。また、プロフィールや問診的なものはアンケート形式で、表示や入力判定を行えば可能になる。 The operation of FIG. 6A will be described in detail below. When the operation of the mobile terminal starts, first, questionnaires and vital information are acquired (S1). When the mobile terminal starts operating, the user can generally perform telephone calls, e-mails, and search for information on the Internet by touch operations and switch operations. In addition, when the user carries the mobile terminal, the user's walking is detected by a built-in vibration sensor or the like, and the user's behavior is recorded by the information of the built-in GPS or mobile phone base station. In addition, the built-in camera makes it possible to determine the user's facial expression and facial color, and the captured image helps to obtain biometric information. In addition, when the wristwatch-type terminal worn by the user is linked, the body temperature can be detected because the wristwatch-type terminal is in contact with the skin. The element emits light, receives reflected light, and the heart rate, blood pressure, etc. can be determined from the reflected change pattern. Vital information is acquired by acquiring these pieces of information. In addition, profiles and questionnaires are in the form of questionnaires, which can be made possible by display and input determination.
　また、ステップＳ１では、制御部２１が、表示部２４に携帯端末２０のユーザに健康状態を問うアンケートを表示すると、ユーザが入力部２３を操作することによって、アンケートに答えることができる。アンケートの内容としては、例えば、健康状態が良いか、それとも不調か、また最近ストレスを感じているか、よく睡眠がとれているか等がある。また、生体情報取得部２６が、ユーザの生体情報を取得する。生体情報としては、例えば、血圧、脈拍、体温等の情報がある。アンケートに対する回答や、生体情報の取得結果は、時計部２５によって出力された日時情報と共に、記録部２９に記録される。 Also, in step S1, when the control unit 21 displays a questionnaire asking the user of the mobile terminal 20 about their health condition on the display unit 24, the user can answer the questionnaire by operating the input unit 23. The contents of the questionnaire include, for example, whether the subject is in good health or not, whether he or she has been feeling stressed recently, and whether he or she is getting good sleep. Also, the biometric information acquisition unit 26 acquires the biometric information of the user. Examples of biological information include blood pressure, pulse, body temperature, and the like. Answers to questionnaires and acquisition results of biometric information are recorded in the recording unit 29 together with date and time information output by the clock unit 25 .
　このステップＳ１は、患者の現在の病状情報に対して、第１の生体情報パターンと、第２の生体情報パターンを関連づけて記録する記録ステップとして機能する。本実施形態においては、症状がなかった期間と、症状が発生している期間における生体情報パターンを比較することによって、症状の原因を突き止めるようにしている（例えば、図８のグラフＧｒ５、Ｇｒ６参照）。このために、生体情報取得部２６がユーザの生体情報を日時情報と共に記録部２９に記録している。上述の第１の生体情報パターンと第２の生体情報パターンは、サーバ１０において特定期間を適宜選択することによって、記録部２９に記録されている生体情報の中から抽出される。もちろん、サーバの求めに応じて、携帯端末がこれを抽出して送信するような構成にしてもよい。 This step S1 functions as a recording step for recording the first biological information pattern and the second biological information pattern in association with the patient's current medical condition information. In this embodiment, the cause of the symptom is identified by comparing the biometric information patterns in the period when the symptom is present and the period when the symptom is not present (see graphs Gr5 and Gr6 in FIG. 8, for example). ). For this purpose, the biometric information acquisition unit 26 records the biometric information of the user in the recording unit 29 together with the date and time information. The first biometric information pattern and the second biometric information pattern described above are extracted from the biometric information recorded in the recording unit 29 by appropriately selecting a specific period in the server 10 . Of course, the configuration may be such that the portable terminal extracts and transmits this in response to a request from the server.
　次に、生活習慣を記録する（Ｓ３）。ここでは、生活情報取得部２７が、携帯端末２０のユーザの日々の生活習慣に関する生活情報を取得するので、制御部２１が、この取得情報を、時計部２５によって出力された日時情報と共に記録部２９に記録する。生活情報としては、例えば、ユーザの位置や動き等を検出し、この検出結果に基づいて、ユーザがジョギング等の運動状態にあるとか、就寝状態にあるとか、オフィスで仕事をしている等、種々の生活情報がある。また、生活情報取得部２７は、サーバ３０等に記録されている情報の中から、携帯端末２０のユーザの生活情報を収集するようにしてもよい。ユーザのストレスを示すような指標があれば、このストレス指標を収集してもよい。 Next, record your lifestyle habits (S3). Here, since the lifestyle information acquisition unit 27 acquires the lifestyle information related to the daily lifestyle habits of the user of the portable terminal 20, the control unit 21 stores this acquired information together with the date and time information output by the clock unit 25. 29. For example, the user's position, movement, etc. are detected as life information, and based on the detection results, information such as whether the user is exercising such as jogging, sleeping, or working in the office. There are various kinds of life information. Also, the life information acquisition unit 27 may collect the life information of the user of the mobile terminal 20 from information recorded in the server 30 or the like. If there is an index that indicates the user's stress, this stress index may be collected.
　次に、生活習慣から推論結果を取得し、表示する（Ｓ５）。後述するように、サーバ１０は、ユーザの生活習慣やバイタル情報や医師による診察等に基づいて、健康状態の改善に関する推論モデルを生成する（図６ＢのＳ３５参照）。ステップＳ５においては、ステップＳ３等において取得した生活習慣等の情報を推論モデルに入力し、ユーザの健康状態を改善するために推論結果を得て、この推論結果を表示部２４に表示する。この推論は、携帯端末２０内に推論エンジンを備えている場合には、携帯端末２０において行ってもよく、備えていない場合には、サーバ１０内において行い、通信部１２、２２を通じて、推論結果を携帯端末２０に送信する。なお、生活習慣に限らず、ステップＳ１において取得した生体情報（バイタル情報）を用いて推論を行ってようにしてもよい。 Next, inference results are obtained from lifestyle habits and displayed (S5). As will be described later, the server 10 generates an inference model regarding the improvement of the health condition based on the user's lifestyle habits, vital information, medical examination by a doctor, etc. (see S35 in FIG. 6B). In step S5, information such as lifestyle habits acquired in step S3 and the like is input to the inference model, inference results are obtained in order to improve the user's health condition, and the inference results are displayed on the display unit 24. This inference may be performed in the mobile terminal 20 if the mobile terminal 20 has an inference engine. to the mobile terminal 20 . It should be noted that inference may be made using the biological information (vital information) acquired in step S1, not limited to lifestyle habits.
　また、サーバ１０に生活習慣から推論を行うことを依頼し、サーバ１０から推論結果を受信し、推論結果を表示部２４に表示してもよい。この場合、サーバ１０が依頼を受ければ直ぐに推論結果を出力するという単純な制御では、健康情報という個人情報が第三者によって簡単に抜き取られてしまう。そこで、図３の表示画面２４ａの「この情報を医師に送りますか？」というような表示を行い、送信前に患者が確認する余裕を設け、表示画面２４ｂ、２４ｃに例示したような送信データ候補を確認できるようにするとよい。患者が送信内容を納得する等、患者の意志を確認してから送信するとよい。また、医師や医療機関からの要望があった場合のみ対応できるように、医療用の通信である事を示す情報や暗号を付与して制約をかけたり、特別なプロトコルにしたりする等、工夫を併せて行えってもよい。暗号として、通信のたびに暗号を付与し、診察時に口頭で、それを伝えるような工夫があってもよい。暗号を作成するのは大変なので、予め記録された候補から選ぶようにしてもよい。 Alternatively, the server 10 may be requested to perform inference based on lifestyle habits, the inference result may be received from the server 10, and the inference result may be displayed on the display unit 24. In this case, if the server 10 is simply controlled to output the inference result immediately upon receiving a request, personal information such as health information can easily be stolen by a third party. Therefore, a message such as "Would you like to send this information to the doctor?" is displayed on the display screen 24a of FIG. Candidates should be verified. It is advisable to confirm the patient's intentions, such as the patient's consent to the contents of the transmission, before transmitting. In addition, in order to be able to respond only when there is a request from a doctor or medical institution, it is necessary to add information indicating that the communication is for medical use or to restrict it by adding encryption, or use a special protocol, etc. You can do it together. As a cipher, a cipher may be added to each communication and verbally communicated during the examination. Since it is difficult to create a cipher, it may be selected from pre-recorded candidates.
　次に、外部依頼があれば、関連情報を検索し、表示すると共に、ユーザ操作に応じてサマリー化して再表示する（Ｓ７）。後述するように、サーバ１０は、ステップＳ２９（図６Ｂ参照）において、症状の原因を検出するための端末情報をサーバに送信するように依頼する。このステップＳ７においては、サーバ１０から依頼があると、制御部２１は、依頼内容に応じた情報を記録部２９内から検索し、ステップＳ８において、通信部２２を通じて、この検索した情報をサーバ１０に送信する。なお、後述するように、依頼された情報が個人情報である場合には、サーバ１０への送信にあたっては、個人情報の送信の許可ステップを設けておくとよい。 Next, if there is an external request, related information is retrieved and displayed, and a summary is displayed according to the user's operation (S7). As will be described later, in step S29 (see FIG. 6B), the server 10 requests the server to transmit terminal information for detecting the cause of symptoms. In this step S7, when there is a request from the server 10, the control unit 21 searches the recording unit 29 for information corresponding to the content of the request. Send to As will be described later, when the requested information is personal information, it is preferable to provide a step of permitting the transmission of the personal information when transmitting the information to the server 10 .
　ステップＳ７では、「外部依頼があれば関連情報検索・表示」とのみ記載したが、実際には、次のようなケースがある。外部依頼が情報項目を指定している場合と、外部依頼が何でも良いから健康関連、生活習慣情報のうち、送信可能なものがあれば送れ、と漠然としている場合である。いずれの場合であっても、その要望に従った情報が記録されていたり、記録されていたものを読み出すことが可能か否かを判定するステップと、情報を収集し、依頼元が利用しやすいように取捨選択したり加工したり、個人のプライヴェートに関わりすぎる情報に対してフィルタリングや情報加工するステップと、何を依頼に応えて送信しようとしているかを、端末ユーザ（患者）が確認しやすいように表示部に表示させるステップ等を有する。なお、情報を利用しやすくするための加工については、端末側で行ってもよいし、医師が扱うシステム（サーバ１０または、それと連携する装置）が行ってもよい。 In step S7, only "search and display related information if there is an external request" was described, but in reality there are the following cases. One is when the external request specifies information items, and the other is when the external request is vague, such as health-related or lifestyle information that can be sent. In any case, the step of determining whether or not the information according to the request is recorded or whether it is possible to read out the recorded information; The terminal user (patient) can easily check the filtering and information processing steps for information that is too related to personal privacy, and what is being sent in response to the request. has a step of displaying on the display unit, and the like. The processing for making the information easier to use may be performed on the terminal side, or may be performed by the system handled by the doctor (the server 10 or a device cooperating therewith).
　また、外部依頼が要求する情報項目でも、場合によっては、送信を躊躇われるケースがあるので、その場合は、送信をしないようにするスイッチ（例えば、送信許可および／または送信拒否のアイコン）等を設けてもよい。また、複数の項目が要求された場合に、送信を許可する項目を選択できるような表示を行い、その選択結果に従って、許可された項目だけを送信することによって、個人が気にするプライヴェートな項目は第三者に知られないようにできる。例えば、人によっては、体重は気にするが、血圧は気にしない人、また、その反対のケースがある。どの項目を送信したいか送信したくないかは人によって異なるので、抽象的なデータ群ではなく、そのデータの項目を明確に表示して選択を可能にようにしてもよい。 Also, even for information items requested by external requests, depending on the case, there may be cases where transmission is hesitant. may be provided. In addition, when multiple items are requested, a display is provided so that the items to be permitted to be transmitted can be selected. can be made invisible to third parties. For example, some people care about their weight but not their blood pressure, and vice versa. Since it differs from person to person as to which items they want to send or not to send, the items of data may be clearly displayed so that they can be selected instead of an abstract data group.
　また、現在は体重を気にしていなくとも、１年前の体重は知られたくない場合もあるので、データの項目で選択できるようにするのみならず、データ取得の時期も明示して、トラブルを防止できるようにしてもい。購買履歴なども、食材、食事に関するものと、その他で分けてもよい。また、購買した場所の情報や何時購入したかなどは、記録されていても送信しないで済むような選択を可能としてもよい。これらの情報はワンセットになっているものから、抜き出して送信するようにしてもよい。また、食事の履歴でも、外食した場所や食材を購入した場所や購入の細かい日時とか時間帯などは知られたくない場合もあり、これらは必要でない場合は削除して情報送信できるようにしてもよい。 Also, even if you don't care about your current weight, there are cases where you don't want people to know your weight a year ago. can be prevented. Purchasing histories and the like may also be divided into those related to foodstuffs and meals, and others. Further, it may be possible to select such that the information on the place of purchase or when the purchase was made does not need to be transmitted even if the information is recorded. These pieces of information may be extracted from one set and transmitted. Also, even in the history of meals, there are cases where you do not want to know the place where you ate out, where you purchased ingredients, the detailed date and time of purchase, etc. good.
　送信に先立って、図３に示すように、患者５１の端末携帯２０の表示部２４の表示画面２４ａに、「この情報を医師に送りますか？」を表示してもよい。また、図３の表示画面２４ｂには「最近の生活情報データ」の具体的数値が表示される表示部があり、表示画面２４ｃには「１年前生活情報データ」の具体的数値が表示される表示部があるで、これらのうち、タッチした方を送るようにしてもよい。このように、項目のみならず、データ取得した日時などの情報も表示して、ユーザが、どのデータであるかを把握しやすくする。個々のデータではなく、そこから読みとれる情報を抽象化して送信してもよい。購入した場所なども特に必要な情報でない場合は削除してもよい。また、同様の項目の複数のデータは、まとめやすいくくりで特定の期間にまとめて抽象化、サマリー（要約）のようなステップを設けてもよい。サマリーにするかしないかを選択できるようにしてもよいし、サマリーにまとめて、抽象化や匿名化が出来たかどうかを、表示することによって確認できるようにしてもよい。このように、ユーザはよく確認してから、送信できるようにした。 Prior to transmission, as shown in FIG. 3, "Would you like to send this information to the doctor?" In addition, the display screen 24b of FIG. 3 has a display section for displaying specific numerical values of "recent living information data", and the display screen 24c displays specific numerical values of "one year ago living information data". Since there is a display unit that touches one of these, the one that is touched may be sent. In this way, not only the items but also information such as the date and time when the data was acquired are displayed, making it easy for the user to grasp which data is. Instead of individual data, information that can be read from it may be abstracted and transmitted. The place of purchase may also be deleted if it is not particularly necessary information. In addition, a plurality of data of similar items may be grouped together in a specific period in an easy-to-collect manner, and steps such as abstraction and summarization may be provided. It may be possible to select whether or not to make a summary, or it may be possible to confirm by displaying whether or not the information has been collected into a summary and abstracted or anonymized. In this way, the user is allowed to send after carefully confirming.
　次に、選択された情報を送信する（Ｓ８）。ここでは、患者の許可を求め、許可があった場合のみ、送信が行われる。つまり、このステップＳ７およびＳ８は、スイッチ表示の操作前に、送信を許可する情報と許可しない情報を選択可能とした送信候補情報を表示する送信候補表示ステップとして機能する。 Next, the selected information is transmitted (S8). Here, the patient's permission is requested, and transmission is performed only when permission is obtained. In other words, steps S7 and S8 function as transmission candidate display steps for displaying transmission candidate information in which transmission-permitted information and transmission-not-permitted information can be selected before the switch display is operated.
　本実施形態は、携帯端末が依頼信号を受信する受信ステップと、依頼に応じて、携帯端末の記録部に記録された情報の中から依頼に応じた情報を検索する検索ステップと、検索された情報を携帯端末の表示部に表示すると共に、表示された情報の依頼元に送信を許可するか否かのスイッチ表示を行う表示制御ステップと、を有する携帯端末の情報共有方法を含んでいる。携帯端末の記録部に記録された情報の中から検索する、依頼に応じた情報は、少なくとも第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンと、を含む情報であってもよい。また、携帯端末の記録部に記録された情報の中から検索する、依頼に応じた情報は、生体情報パターンの時間変化に基づいた情報であってもよい。さらに、携帯端末に記録された生体情報パターンからストレスを判定するストレス判定ステップを有し、携帯端末の記録部に記録された情報の中から検索する、依頼に応じた情報は、ストレス情報であってもよい。 This embodiment includes a receiving step in which the mobile terminal receives a request signal, a searching step in which, in response to the request, information corresponding to the request is searched from information recorded in a recording unit of the mobile terminal, a display control step of displaying information on a display unit of the mobile terminal and displaying a switch indicating whether or not to permit transmission of the displayed information to a requester of the displayed information. The requested information to be retrieved from the information recorded in the recording unit of the mobile terminal includes at least a first biometric information pattern in a specific period going back at least a first time and a second biometric information pattern going back a second time in a specific period. and the information including the biological information pattern of . Further, the requested information to be retrieved from the information recorded in the recording unit of the mobile terminal may be information based on the time change of the biometric information pattern. Further, a stress determination step is provided to determine stress from the biometric information pattern recorded in the mobile terminal, and the requested information retrieved from the information recorded in the recording unit of the mobile terminal is stress information. may
　次に、特定タイミングや特定の状況であるか否かを判定する（Ｓ９）。特定のタイミングや特定の状況としては、例えば、携帯端末１０のユーザが、過去または現在における生体情報や生活情報等のエビデンスを確認したり、また健康に関するアドバイスを見たい場合がある。この場合には、ユーザが携帯端末２０の入力部２３等の操作部材を操作する。また、携帯端末２０や外部装置（例えば、サーバ１０）が、特定タイミングや特定状況として、自動的にトリガを掛けるようにしてもよい。この自動的なトリガは、例えば、所定期間間隔に発生してもよく、また生体情報や生活習慣を表す情報が、所定値を超えた時であってもよい。この判定の結果、特定タイミングや特定の状況でない場合には、ステップＳ１に戻る。 Next, it is determined whether there is a specific timing or a specific situation (S9). As specific timing or specific situation, for example, the user of the mobile terminal 10 may want to check past or present evidence such as biometric information or life information, or want to see health advice. In this case, the user operates the operation member such as the input unit 23 of the mobile terminal 20 . Also, the mobile terminal 20 or an external device (for example, the server 10) may automatically apply a trigger as a specific timing or specific situation. This automatic trigger may occur, for example, at predetermined time intervals, or may occur when biometric information or information representing lifestyle habits exceeds a predetermined value. As a result of this determination, if it is not the specific timing or the specific situation, the process returns to step S1.
　一方、ステップＳ９における判定の結果、特定タイミングや特定状況の場合には、エビデンス表示を行う（Ｓ１１）。後述するように、サーバ１０は特定期間の代表値の比較によってエビデンス判定を行う（図６Ｂの Ｓ３１参照）。ここでは、制御部２１が、サーバ１０が行ったエビデンス比較の結果を表示部２４に表示させる。この表示では、特定期間の代表値を比較できるように（例えば、過去の１年前の生体情報や生活習慣情報と、現在の生体情報や生活習慣情報を対比して）表示する。なお、携帯端末２０内の制御部２１等が、特定期間におけるエビデンスを比較して表示してもよいし、また現在、または過去のエビデンスを表示するようにしてもよい。 On the other hand, if the result of determination in step S9 is a specific timing or specific situation, evidence is displayed (S11). As will be described later, the server 10 performs evidence determination by comparing representative values for a specific period (see S31 in FIG. 6B). Here, the control unit 21 causes the display unit 24 to display the result of the evidence comparison performed by the server 10 . In this display, representative values for a specific period can be compared (for example, the biometric information and lifestyle information of the past one year ago are compared with the current biometric information and lifestyle information). Note that the control unit 21 or the like in the mobile terminal 20 may compare and display evidence in a specific period, or may display current or past evidence.
　次に、改善アドバイス、服薬アドバイス等を行う（Ｓ１３）。後述するように、サーバ１０は、裏付けデータから処方を行う（図６ＢのＳ３３参照）。ここでは、制御部２１が、サーバ１０が行った処方に基づいて、改善アドバイスや、服薬アドバイスを表示部２４に表示させる。この表示を行うと、ステップＳ１に戻る。 Next, advice on improvement, medication advice, etc. are given (S13). As will be described later, the server 10 prescribes from the supporting data (see S33 in FIG. 6B). Here, the control unit 21 causes the display unit 24 to display improvement advice and medication advice based on the prescription made by the server 10 . After this display, the process returns to step S1.
　次に、図６Ｂに示すフローチャートを用いて、サーバ１０における動作を説明する。図６Ｂのフローは、サーバ１０内の制御部１１が、メモリに記憶されたプログラムに従ってサーバ１０内の各部を制御することによって実現する。 Next, the operation of the server 10 will be described using the flowchart shown in FIG. 6B. The flow of FIG. 6B is realized by control unit 11 in server 10 controlling each unit in server 10 according to a program stored in the memory.
　サーバの動作が開始すると、まず、患者情報を入力する（Ｓ２１）。サーバ１０は、病院等の医療施設内に配置されたサーバ、または医療施設等の外部に配置され、医療施設等内の機器と通信可能に接続されたサーバを想定している。このステップでは、医療施設等に診察を受けに来た患者の情報を入力する。患者情報としては、例えば、氏名、年齢、性別、既往症等、患者の診察に必要な基礎情報を含む。 When the server starts operating, first, patient information is entered (S21). The server 10 is assumed to be a server located within a medical facility such as a hospital, or a server located outside the medical facility or the like and communicably connected to devices within the medical facility or the like. In this step, the information of the patient who came to the medical facility or the like to be examined is entered. The patient information includes, for example, basic information necessary for patient diagnosis, such as name, age, sex, and pre-existing diseases.
　次に、問診、検査、診察を行う（Ｓ２３）。ここでは、医師等が患者に問診を行い、必要な検査を行い、診察等を行う。これらの一連の動作においては、医師等がＰＣ等のコンピュータに、必要な情報を手動で入力してもよく、また、機器等から自動的に情報を入力してもよい。さらに、例えば、問診等の際の音声データをテキストデータに自動的に変換して、入力するようにしてもよい。 Next, interviews, examinations, and examinations are performed (S23). Here, a doctor or the like interviews the patient, performs necessary examinations, and examines the patient. In this series of operations, a doctor or the like may manually input necessary information into a computer such as a PC, or the information may be automatically input from a device or the like. Further, for example, voice data for a medical interview may be automatically converted into text data for input.
　次に、医師の問診や診断がなされたか否かを判定する（Ｓ２５）。ここでは、制御部２１は、ステップＳ２３において入力された情報に基づいて、医師等が問診を行ったか、また診断を行ったかを判定する。例えば、医師が問診の結果、運動不足による便秘かもしれないと診断する場合がある。このステップＳ２５における判定の結果、問診や診断がなかった場合には、ステップＳ２１に戻る。 Next, it is determined whether or not a doctor's interview or diagnosis has been made (S25). Here, based on the information input in step S23, the control unit 21 determines whether the doctor or the like conducted an interview or made a diagnosis. For example, a doctor may diagnose constipation due to lack of exercise as a result of an interview. If the result of determination in step S25 is that there has been no questioning or diagnosis, the process returns to step S21.
　一方、ステップＳ２５における判定の結果、医師による問診、診断があった場合には、次に、問診内容や症状や診断結果に基づいて、原因の候補一覧を検索する（Ｓ２７）。ここでは、制御部１１は、記録・ＯＢ部１９内に記録されている疾病別原因１９ｄを検索する。すなわち、医師が問診を行い、検査を行うことによって、患者の症状が分かる。この症状が生じた原因が分かれば、この原因に応じた処方を行えばよい。そこで、このステップでは、制御部１１が、症状等とその原因を記録した記録・ＤＢ部１９の疾病別原因１９ｄ（図７参照）に記録されている原因候補を検索する。例えば、ステップＳ２５において、運動不足による便秘と診断された場合には、このステップでは、本当に運動不足が原因であるかという仮説をたてて検索してもよい。原因候補は、ストレス、運動不足、不規則な生活、摂取物、気候の内の少なくとも１を含んでいる。 On the other hand, if the result of determination in step S25 is that there is an interview or diagnosis by a doctor, then a list of candidate causes is searched based on the contents of the interview, symptoms, and diagnosis results (S27). Here, the control unit 11 searches the disease-specific cause 19 d recorded in the recording/OB unit 19 . In other words, the doctor can understand the symptoms of the patient by interviewing and examining the patient. If the cause of this symptom is known, the prescription should be made according to this cause. Therefore, in this step, the control unit 11 searches for possible causes recorded in the disease-specific causes 19d (see FIG. 7) of the recording/DB unit 19 in which symptoms and their causes are recorded. For example, if constipation due to lack of exercise is diagnosed in step S25, then in this step, a search may be made based on a hypothesis as to whether the lack of exercise is really the cause. Candidate causes include at least one of stress, lack of exercise, irregular lifestyle, diet, and climate.
　原因候補を一覧から検索すると、次に、原因を検証するための端末情報の送付を患者端末に依頼する（Ｓ２９）。ステップＳ２７において、疾病別原因１９ｄを検索することによって、患者の症状に基づいて原因の候補が挙げられ、この原因が本当に症状の原因であるかを検証するためのエビデンスデータが分かる（図７参照）。そこで、このステップでは、患者が日頃持ち歩いている携帯端末２０に対して、原因を検証するために、エビデンスデータを送信するように依頼する。前述した運動不足による便秘と診断した場合には、ここでは、運動不足であることを検証するためのエビデンスの送信を携帯端末２０に依頼することになる。このステップＳ２９は、第１および第２の生体情報パターンの取得に先立って、患者の有する携帯端末に対して第１および第２の生体情報パターンを取得する旨を通知する通信ステップとして機能する。 After searching for cause candidates from the list, the patient terminal is then requested to send terminal information for verifying the cause (S29). In step S27, by searching the disease-specific causes 19d, candidate causes are listed based on the patient's symptoms, and evidence data for verifying whether these causes are really the causes of the symptoms can be obtained (see FIG. 7). ). Therefore, in this step, the portable terminal 20 that the patient usually carries around is requested to transmit evidence data in order to verify the cause. If constipation due to lack of exercise is diagnosed as described above, the portable terminal 20 is requested to transmit evidence for verifying lack of exercise. This step S29 functions as a communication step for notifying the mobile terminal of the patient that the first and second biometric information patterns are to be acquired, prior to acquisition of the first and second biometric information patterns.
　また、ステップＳ２９では、サーバ１０からの要求に応じて、携帯端末２０から送信されてくるエビデンスデータを受信する。ここでは、携帯端末２０は、要求のあったエビデンスデータについて現在から過去に遡って生体情報や生活習慣情報が送信されてくる。このステップＳ２９は、第１の時間遡る特定期間（例えば、数カ月前）の第１の生体情報パターン（例えば、心拍数のパターン）と、第２の時間遡る特定期間（例えば、1年前の数カ月）の第２の生体情報パターン（例えば、新派数のパターン）を取得する取得ステップとして機能する。また、ステップＳ２９は、医師の問診または診断に従って、疾患の原因となる候補を検索し、この原因候補を検証するためのエビデンスデータの送付を患者の携帯端末に要求する要求ステップとして機能する。ここで、エビデンスデータは、患者の生体情報データおよび／または生活習慣情報データである。 Also, in step S29, evidence data transmitted from the mobile terminal 20 is received in response to a request from the server 10. Here, the portable terminal 20 transmits biological information and lifestyle information retroactively from the present to the past with respect to the requested evidence data. In this step S29, a first biometric information pattern (for example, a heart rate pattern) in a first specific time period (for example, several months ago) and a second specific time period (for example, several months one year ago) ) to obtain a second biometric information pattern (for example, a new number pattern). In addition, step S29 functions as a requesting step of searching for a candidate cause of a disease according to an inquiry or diagnosis by a doctor and requesting the patient's mobile terminal to send evidence data for verifying this candidate cause. Here, evidence data is a patient's biometric information data and/or lifestyle information data.
　エビデンスデータを携帯端末に依頼すると、次に、特定期間の代表値比較でエビデンス判定を行う（Ｓ３１）。原因候補が、本当に疾病の原因であったか否かを判定するにあたって、過去、患者に症状が発生していなかった時期におけるエビデンスデータと、症状が発生している時期におけるエビデンスデータを比較してみるのが分かり易い（例えば、後述する図８および図９参照）。そこで、このステップでは、特定期間におけるエビデンスデータの代表値を比較し、症状の原因を判定する。特定期間は、診察時には分からないことが多い。そこで、所定期間ずつエビデンスデータを比較しながら、過去に遡り、現在と過去との差異がはっきりした時期を特定期間とする。前述の運動不足による便秘と診断した場合には、このステップでは実際に運動不足であるか否かをエビデンスデータに基づいて検証する。このステップにおける動作の詳細については、図１０を用いて後述する。 After requesting the mobile terminal for the evidence data, next, the evidence is determined by comparing the representative values for a specific period (S31). In determining whether or not the candidate cause was really the cause of the disease, it is important to compare the evidence data in the past when the patient had no symptoms and the evidence data when the patient had symptoms. is easy to understand (for example, see FIGS. 8 and 9 described later). Therefore, in this step, representative values of evidence data in a specific period are compared to determine the cause of symptoms. The specific period is often unknown at the time of examination. Therefore, while comparing the evidence data for each predetermined period, we go back to the past and set the period when there is a clear difference between the present and the past as the specific period. If the above-mentioned constipation due to lack of exercise is diagnosed, in this step it is verified based on evidence data whether or not it is actually due to lack of exercise. Details of the operation in this step will be described later with reference to FIG.
　この特定期間のエビデンスデータの代表値の判定にあたっては、この期間におけるデータが時間と共に変化することによって生成されるパターンを比較してもよい。症状の原因の判定としては、例えば、患者がストレスを感じていたか、また運動不足であったか、生活が不規則であったか等（例えば、図７参照）がある。このステップＳ３１は、第１の生体情報パターンと第２の生体情報パターンを比較し、この比較に基づいて、患者のストレスを判定するストレス判定ステップとして機能する。また、ステップＳ３１は、携帯端末から上記エビデンスデータを受信すると、該エビデンスデータについての、第１の時間遡る特定期間における第１のパターンと、第２の時間遡る特定期間における第２のパターンを比較する比較ステップとして機能する。 In determining the representative value of evidence data for this specific period, patterns generated by changes in the data over time in this period may be compared. Determination of the cause of symptoms includes, for example, whether the patient was stressed, lacked exercise, or had an irregular lifestyle (see, for example, FIG. 7). This step S31 compares the first biometric information pattern and the second biometric information pattern, and functions as a stress determination step of determining the patient's stress based on this comparison. Further, in step S31, when the evidence data is received from the portable terminal, the first pattern in the specific period of time going back for the first time and the second pattern of the specific period going back to the second time are compared for the evidence data. Acts as a comparison step to
　また、特定期間において、第１および第２の生体情報パターンの代表値を比較することによって行う原因エビデンス判定は、候補となる原因毎に遡った時期における第１および第２の生体情報パターンの差異を調べる。原因エビデンス判定において、遡る時期は、候補となる原因の差異を調べ、差異が明確な時期とする（例えば、図１０のＳ４５参照）。 Further, in the specific period, the cause evidence judgment performed by comparing the representative values of the first and second biometric information patterns is based on the difference between the first and second biometric information patterns in the period retroactively for each candidate cause. to examine. In the causal evidence determination, the time to go back is the time when differences in candidate causes are investigated and the differences are clear (for example, see S45 in FIG. 10).
　エビデンスデータとして、図４Ａに示すような心電図の場合には、第１及び第２の生体情報パターンは、心電図のＲ波最大値時点間の 間隔を、直前に求められたものと比較可能なパターンである。また、エビデンスデータとして、図５に示すような血圧図の場合には、第１および第２の生体情報パターンは、一日の血圧変化のピーク値と回数が比較可能なパターンである。また、ストレスの判定は、第１および第２の生体情報パターンの揺らぎを比較することによって行う。なお、生体情報は、生データのみならず、それを加工したり解釈したりしたものを含んでもよい。 As evidence data, in the case of an electrocardiogram as shown in FIG. 4A, the first and second biological information patterns are patterns that can be compared with the immediately preceding interval between the R-wave maximum values of the electrocardiogram. is. As evidence data, in the case of a blood pressure chart as shown in FIG. 5, the first and second biometric information patterns are patterns in which the peak value and frequency of blood pressure changes in a day can be compared. Moreover, determination of stress is performed by comparing the fluctuations of the first and second biometric information patterns. The biometric information may include not only raw data but also processed or interpreted data.
　エビデンスを用いて判定すると、次に、裏着けデータから処方する（Ｓ３３）。ステップＳ３１におけるエビデンス判定の結果、医師が想定した症状の原因について検証できた場合には、その症状の原因を除去するために、必要な処方、例えば、服薬や、食事指導、運動指導等の処方を出す。前述の運動不足による便秘と診断した場合であって、ステップＳ３１においてその診断が検証された場合には、運動不足という原因を前提にして患者に処方すればよい。 After making a judgment using the evidence, it is then prescribed based on the backing data (S33). As a result of the evidence determination in step S31, if the cause of the symptom assumed by the doctor can be verified, a prescription necessary to eliminate the cause of the symptom, such as medication, dietary guidance, exercise guidance, etc. out. If constipation due to lack of exercise is diagnosed as described above and the diagnosis is verified in step S31, the cause may be presumed to be lack of exercise and prescribed to the patient.
　例えば、ストレスの判定結果に基づいて、患者への治療方法を決定する。ここで出された処方は、通信部１２、２２を通じて、携帯端末２０に送信され、携帯端末２０の表示部２４に改善アドバイスや服薬アドバイスが表示される（図６ＡのＳ１３参照）。すなわち、患者への処方を、携帯端末に送信している。ステップＳ３１は、第１のパターンと第２のパターンの比較結果に基づいて、原因候補の中から患者の疾患の原因を決定する決定ステップとして機能する。この原因から、患者自らが、生活等を改善する場合もあるが、医師が処方をしてもよく、その場合は、決定された原因に基づいて、患者に処方する処方ステップとして機能する。 For example, based on the stress assessment results, determine the treatment method for the patient. The prescription issued here is transmitted to the mobile terminal 20 through the communication units 12 and 22, and improvement advice and medication advice are displayed on the display unit 24 of the mobile terminal 20 (see S13 in FIG. 6A). That is, the prescription for the patient is transmitted to the portable terminal. Step S31 functions as a determination step for determining the cause of the patient's disease from the cause candidates based on the result of comparison between the first pattern and the second pattern. In some cases, the patient himself/herself improves his/her life based on this cause, but the doctor may prescribe it, and in that case, it functions as a prescribing step of prescribing to the patient based on the determined cause.
　次に、エビデンスデータと、症状・診断結果を関連付け、教師データを作成し、この教師データを用いて推論モデルを作成し、患者の健康の改善処理を行う（Ｓ３５）。ここでは、患者の健康改善を行う際に使用する推論モデルの作成を行う。ステップＳ２１～Ｓ３３において、患者の症状と診断結果に関するデータを多数収集すると、これらのデータに基づいて教師データを作成することができる。サーバ１０内に推論モデル生成用の学習装置が設けてあれば、この学習装置を用いて、またサーバ１０内に学習装置が設けてない場合には、外部サーバ３０等に設けられている学習装置を用いて、患者の健康改善を行うための推論モデルを生成する。この推論モデルを携帯端末２０内の推論エンジンに設定すれば、携帯端末２０において、ユーザに健康アドバイスを行うことができる（図６ＡのＳ５参照）。推論モデルの生成については、図１１を用いて後述する。 Next, the evidence data and symptoms/diagnosis results are associated to create teacher data, an inference model is created using this teacher data, and the patient's health is improved (S35). Here, we will create an inference model that will be used to improve patient health. In steps S21 to S33, when a large amount of data regarding patient symptoms and diagnostic results is collected, teacher data can be created based on these data. If a learning device for inference model generation is provided in the server 10, this learning device is used. If no learning device is provided in the server 10, a learning device provided in the external server 30 or the like is used. is used to generate an inference model for patient health improvement. By setting this inference model in the inference engine in the mobile terminal 20, health advice can be given to the user in the mobile terminal 20 (see S5 in FIG. 6A). Generation of an inference model will be described later with reference to FIG.
　なお、サーバ１０は、多数の携帯端末２０と接続可能であり、これら携帯端末２０からエビデンスデータ（端末情報）を収集することができる。さらに、サーバ１０は、他の同様なサーバからもエビデンスデータ（端末情報）を収集してもよい。多数のエビデンスデータを収集することによって、信頼性の高い推論モデルを生成することが可能となる。ステップＳ３５において推論モデルの作成等を行うと、ステップＳ２１に戻る。 The server 10 can be connected to a large number of mobile terminals 20, and can collect evidence data (terminal information) from these mobile terminals 20. Furthermore, the server 10 may also collect evidence data (terminal information) from other similar servers. By collecting a large amount of evidence data, it becomes possible to generate a highly reliable inference model. After creating an inference model in step S35, the process returns to step S21.
　このように、本実施形態においては、携帯端末２０がユーザの生体情報や生活習慣を日々記録している（図６ＡのＳ１、Ｓ３参照）。このユーザが医師等の診察を受けた際に、医師の診断結果を確かめるための原因候補を検索し（図６ＢのＳ２３～Ｓ２７）、原因候補が本当に原因かを検証するために、ユーザの携帯端末２０にエビデンスデータの提供を依頼する（図６ＡのＳ７、図６ＢのＳ２９）。サーバ１０は、携帯端末２０から提供されたエビデンスデータを分析し、この分析結果、裏付けがとれたデータに基づいて、ユーザに服薬アドバイスを提示し、また食事・運動アドバイス等の改善アドバイスを提示している（図６ＢのＳ２３、図６ＡのＳ１３）。このため、本実施形態においては、医師の診察等に基づく処方を、ユーザの端末携帯２０に記録されているエビデンスデータに基づいて検証しているので、確かな処方を行うことができる。 Thus, in the present embodiment, the mobile terminal 20 records the user's biometric information and lifestyle on a daily basis (see S1 and S3 in FIG. 6A). When this user receives a medical examination by a doctor or the like, the user searches for cause candidates to confirm the doctor's diagnosis result (S23 to S27 in FIG. 6B), and checks whether the cause candidate is really the cause. The terminal 20 is requested to provide evidence data (S7 in FIG. 6A, S29 in FIG. 6B). The server 10 analyzes the evidence data provided from the mobile terminal 20, and presents medication advice to the user based on the results of this analysis and corroborated data, as well as improvement advice such as diet/exercise advice. (S23 in FIG. 6B, S13 in FIG. 6A). Therefore, in the present embodiment, the prescription based on the doctor's examination or the like is verified based on the evidence data recorded in the user's terminal mobile phone 20, so that a reliable prescription can be made.
　また、本実施形態においては、ユーザのエビデンスデータに基づいて裏付けのとれた診断結果に基づいて、推論モデルを生成するようにしている（図６ＢのＳ３５）。そして、ユーザは日々、この推論モデルと生活習慣等のデータに対して、健康状態の改善アドバイスを受けることができる（図６ＡのＳ５）。このため、信頼性の高い推論モデルによって、健康状態の改善アドバイスを受けることができる。 Also, in this embodiment, an inference model is generated based on diagnostic results that are backed up by the user's evidence data (S35 in FIG. 6B). Then, the user can receive advice for improving the health condition on a daily basis with respect to this inference model and data such as lifestyle habits (S5 in FIG. 6A). For this reason, it is possible to receive advice for improving the state of health using a highly reliable inference model.
　次に、図７を用いて、症状に基づく原因候補、そのエビデンスデータ、およびエビデンスデータを取得するための機器について説明する。図６ＢのステップＳ２７、Ｓ２９において、医師等の診察に基づく仮説を検出するためにエビデンスデータの提供を患者端末に依頼している。この図７に示す図表は、患者の症状から予想される原因と、この原因を検証するためのエビデンスデータと、このエビデンスデータを取得するための機器の関係を示している。 　Next, using Fig. 7, we will explain cause candidates based on symptoms, their evidence data, and devices for acquiring the evidence data. In steps S27 and S29 of FIG. 6B, the patient terminal is requested to provide evidence data in order to detect hypotheses based on medical examination by a doctor or the like. The chart shown in FIG. 7 shows the relationship between the cause expected from the patient's symptoms, the evidence data for verifying this cause, and the equipment for acquiring this evidence data.
　図７において表形式で記載したものは、特定の疾病の原因と言われるもののうち、生活習慣やストレス等に起因し、比較的改善が可能なものを優先的に記載し、その改善可能な項目ごとに、その裏付けとなり得ると考えられるデータ（バイタルデータや生活や行動が反映されるデータであって、携帯端末が取得するメリットがある、日常において時間的な測定を行うことに意味を持つもの）を想定してまとめたものである。また、そのデータが取得できる対応機器を示した。この対応機器は、患者、被検者など一般ユーザが所持しやすいものを挙げている。血液検査、尿検査のような検体検査なども将来的には、簡便な方法が出来るようになれば、この表に入れてもよい。ただ、遺伝子検査は、改善が困難な要素を含み、時間的に変わるものではないので、携帯端末に依存する必要はなく、ここには含んでいない。病院やクリニックで取得して足りるものは、この表では優先的に採用していない。 In the table format shown in FIG. 7, among the causes of specific diseases, those that are caused by lifestyle habits, stress, etc. and can be relatively improved are given priority, and the items that can be improved are listed. data (vital data, data reflecting lifestyles and behaviors, which are advantageous for mobile terminals to obtain, and which are meaningful for time measurement in daily life). ) is assumed. In addition, the compatible devices that can acquire the data are shown. The compatible devices are devices that are easy for general users such as patients and subjects to possess. Sample tests such as blood tests and urine tests may also be included in this table in the future if simpler methods become available. However, genetic testing includes elements that are difficult to improve and does not change over time, so it does not need to rely on mobile terminals and is not included here. Those that can be obtained at hospitals and clinics are not given priority in this table.
　また、ここでは表形式で、考え方のロジックを明記したが、必ずしも、表形式（いわばデータベース形式）でまとめなくとも、何らかのルールベースで分岐して判定してもよく（定められたプログラムに従うもの）、あるいは学習された推論モデルで導くものであってもよい。また、意外な要因が絡み合ったり、意外な事象が契機となったりして発病する場合もあり、また感染症等もあるため、行動履歴や環境（温湿度など、天気、災害、人との密集度）等もすべて判定するようにしてもよい。ただし、多くのものは、問診で済む。 Also, here, the logic of the idea is specified in a table format, but it is not necessary to organize it in a table format (database format, so to speak), and it is possible to branch and judge based on some rule (following a set program). , or derived from a learned inference model. In addition, unexpected factors may be intertwined or unexpected events may trigger the onset of the disease. degree), etc. may also be determined. However, in most cases, an interview is enough.
　図７に戻り、この表は、特に病気の原因とされながら、診察の時点のみでは、エビデンスとして取得しにくい項目（つまり日常で簡単に得られるデータ）、問診でも時間変化や他の人との比較が分かりにくい項目を優先している。表の最左欄には、患者の症状が記載され、その右隣には症状の原因となる候補が記載され、さらにその右隣には、原因が正しいかどうかを検出するためのエビデンスデータが記載され、最右欄には、エビデンスデータを検出するための機器が記載されている。例えば、患者の症状が、腹痛・便秘等の消化器系である場合には、この症状が発生した原因として、（１）運動不足、（２）摂取物、（３）生活が不規則、（４）ストレスが想定される。運動不足が原因と想定する場合、このことを確かめるには、万歩計（登録商標）のデータや患者の心拍数のデータを見ればよい。万歩計（登録商標）のデータは、患者が使用しているスマートフォンの万歩計（登録商標）アプリ等によって入手することができ、また心拍数は、患者が使用しているスマートウォッチ等によって入手することができる。その他の例については、図７から分かるので、詳しい説明は省略する。 Returning to Figure 7, this table shows items that are considered to be the cause of illness, but are difficult to obtain as evidence only at the time of examination (that is, data that can be easily obtained on a daily basis), changes over time in interviews, and interactions with other people. Priority is given to items that are difficult to compare. The patient's symptoms are listed in the leftmost column of the table, and to the right of it are listed candidates for the cause of the symptoms. The rightmost column lists the equipment used to detect the evidence data. For example, if the patient's symptom is a digestive system such as abdominal pain, constipation, etc., the cause of this symptom may be (1) lack of exercise, (2) food intake, (3) irregular lifestyle, ( 4) Stress is assumed. If you assume that lack of exercise is the cause, you can check this by looking at pedometer data or the patient's heart rate data. The pedometer (registered trademark) data can be obtained from the pedometer (registered trademark) app on the smartphone used by the patient, and the heart rate can be obtained from the smart watch used by the patient. can be obtained. Other examples can be understood from FIG. 7, and detailed description thereof will be omitted.
　なお、心拍数は、睡眠計測機能付きのスマートウォッチやフィットネストラッカ等によって取得することができる。スマートウォッチ等の光学式心拍計は、グリーンの光をユーザに腕の皮膚の表面に投射し、その反射光を計測することによって血流量を測定し、血流量の増減から心拍数を算出する。また、スマートウォッチ等は光学式心拍系に加えて、腕の動きの検知する「加速度センサ」を備え、この両方を使用して睡眠状態を計測、分析している。この分析に基づいて、起きている状態（覚醒状態）と寝ている状態（睡眠状態）の２つの状態を区別できる。また、分析に基づいて、睡眠の質を、「浅い眠り」「深い眠り」「レム睡眠」「覚醒」などのレベルに分けることもできる。 In addition, the heart rate can be obtained by a smartwatch with a sleep measurement function, a fitness tracker, etc. An optical heart rate monitor such as a smart watch projects green light onto the skin surface of the user's arm, measures the blood flow by measuring the reflected light, and calculates the heart rate from the increase or decrease in the blood flow. In addition to the optical heart rate system, smart watches and the like are equipped with an "acceleration sensor" that detects the movement of the arm, and both of these are used to measure and analyze sleep conditions. Based on this analysis, two states can be distinguished: the waking state (wake state) and the sleeping state (sleep state). Based on the analysis, sleep quality can also be divided into levels such as "light sleep", "deep sleep", "REM sleep", and "wake".
　次に、図８を用いて、医師等が患者を診察する際に、患者の携帯端末２０に記録されている生体情報や生活習慣情報に基づいて、症状・疾病等の原因を説明し、治療のためのアドバイスを行う様子について説明する。図８に示す例は、ポリープが発見された患者に対して、携帯端末２０に記録されている生体情報や生活習慣情報を用いて、医師がポリープの発生した原因を診断し、この原因を考慮して患者に対して、アドバイスを与えるものである。 Next, referring to FIG. 8, when a doctor or the like examines a patient, based on the biometric information and lifestyle information recorded in the patient's portable terminal 20, the cause of the symptom/disease is explained, and treatment is performed. I will explain how to give advice for In the example shown in FIG. 8, the doctor diagnoses the cause of the polyp using biometric information and lifestyle information recorded in the mobile terminal 20 for the patient in whom the polyp is found, and considers the cause. and give advice to the patient.
　図８は、医師５３がＰＣ１０Ａを見ながら、患者の疾患（この例では、ポリープの発生）の原因について、患者に説明している。この説明を行う前に、医師５３は患者の疾患（ポリープ発生）の原因の候補をいくつか想定し、この原因の候補を検証するためのデビデンスデータ（この例では、ストレスに関するデータと、睡眠時間に関するデータ）を、患者の携帯端末２０からサーバ１０に送信してもらっている（図６ＢのＳ２７、Ｓ２９、図６ＡのＳ７参照）。サーバ１０は、患者からエビデンスデータを受信すると、特定期間の代表値を比較し、想定した原因が確かどうかについて判定している（図６ＢのＳ３１、Ｓ３３参照）。特定期間の代表値は、この期間の個々の値でもよく、また期間の平均値でもよく、また日単位に限らず時間帯でもよい。 In FIG. 8, the doctor 53 explains to the patient the cause of the patient's disease (the occurrence of polyps in this example) while looking at the PC 10A. Before giving this explanation, the doctor 53 assumes several candidates for the cause of the patient's disease (occurrence of polyps), and provides evidence data (in this example, data on stress and sleep time) is transmitted from the patient's portable terminal 20 to the server 10 (see S27 and S29 in FIG. 6B and S7 in FIG. 6A). When receiving the evidence data from the patient, the server 10 compares the representative values of the specific period and determines whether the assumed cause is certain (see S31 and S33 in FIG. 6B). The representative value of a specific period may be an individual value of this period, an average value of the period, and may be a time zone as well as a day unit.
　図８に示す例では、ＰＣ１０ＡにグラフＧｒ５として、特定期間として昨年のある期間のストレス信号ＳｉｇＬＹと、最近のある期間のストレス信号ＳｉｇＣが示されている。このストレス信号は、図７に示したように、血圧や心電図が利用される。また、図８に示す例では、ＰＣ１１０ＡにグラフＧｒ６として、自覚前から最近までの間における睡眠時間が示されている。自覚前は午後１１時前に就寝し、午前７時過ぎに起床している。しかし、最近では午後１１時過ぎに就寝し、午前７時前に起床しており、明らかに最近では睡眠時間が減少している。 In the example shown in FIG. 8, the graph Gr5 on the PC 10A shows the stress signal SigLY for a certain period of the last year and the stress signal SigC for a recent certain period as the specific period. Blood pressure and an electrocardiogram are used as this stress signal, as shown in FIG. Further, in the example shown in FIG. 8, the PC 110A shows a graph Gr6 indicating sleep hours from before awareness to the latest. Before I realized it, I went to bed before 11:00 pm and woke up after 7:00 am. However, these days, he goes to bed after 11:00 pm and wakes up before 7:00 am, so it is clear that he sleeps less these days.
　医師５３は、グラフＧｒ５、Ｇｒ６を用いて、ストレスや睡眠時間に関して過去と現在のエビデンスを比較することによって、患者の疾患（ポリープの発生）の原因がストレスであることが分かる。原因が分かれば、患者に対して、ストレスを減少させるために、（１）深呼吸をする、（２）適度な運動をする、（３）良質な睡眠をとる、という３つのアドバイスを与えることができる。 　Physician 53 uses graphs Gr5 and Gr6 to compare past and present evidence regarding stress and sleep time, and finds that stress is the cause of the patient's disease (occurrence of polyps). Once the cause is known, three pieces of advice can be given to the patient to reduce stress: (1) take deep breaths, (2) exercise moderately, and (3) get good quality sleep. can.
　図９は、患者の有する携帯端末２０に、生体情報パターンを表示した例を示す。この画面を医師に見せて診断してもらってもよく、これと同様の表示が医師の端末に表示ができるように、このようなデータを送る際に、表示した例にもなっている。つまり、医師が、これまでのカルテに欠けた情報として、必要なデータを要求するような場合、携帯端末２０が医師からの依頼信号を受信すると、携帯端末２０は記録部２９に記録されている情報の中から依頼に応じた情報を検索し、検索された情報を携帯端末２０の表示部２４に表示する。図９に示す例では、数値そのものを表示するのではなく、グラフにしたものを表示している。本実施形態は、グラフ化がなされるようにデータのグラフ化ステップを有する情報共有方法の例を含んでいる。 FIG. 9 shows an example of a biological information pattern displayed on the portable terminal 20 of the patient. This screen may be shown to a doctor for diagnosis, and the same display as this can be displayed on the doctor's terminal. In other words, when the doctor requests necessary data as information missing in the previous chart, when the mobile terminal 20 receives the request signal from the doctor, the mobile terminal 20 records it in the recording unit 29. The requested information is retrieved from the information, and the retrieved information is displayed on the display unit 24 of the portable terminal 20.例文帳に追加In the example shown in FIG. 9, a graph is displayed instead of the numerical value itself. This embodiment includes an example of an information sharing method having a step of graphing the data as graphing occurs.
　図９に示すように、グラフを表示するようにすれば、単にデータの集まりが表示されるより、具体的なイメージが把握しやすく、医師がその表示を見て診断の一助にし易い。また、患者自身が、そこに表示されているデータを医師にゆだねて良いか否かについて、簡単に判断できるというメリットがある。表示されたグラフの中に、気に入らない部分があれば、そこを削除して提供するなども簡単に出来る。図示していないが、送信時に、「部分削除して送信」、というような操作が出来るようにして、タッチされた部分は送信しないようにする工夫も可能である。その場合、患者によって、その部分のデータは共有されなかった、というような表示を医師に伝えるようにする。また、図示していないが、データ取得の日時データなどもエビデンスとして表示することは言うまでもない。また、医師としても、結局、診断の助けになる情報が得られないのに、患者の生体情報ばかりを集めてもノイズにしかならないので、患者端末で目視確認してから、「その情報をください」、と送信を依頼するようにしてもよい。 As shown in FIG. 9, if a graph is displayed, it is easier for a doctor to grasp a specific image rather than simply displaying a collection of data, and the doctor can see the display to help with diagnosis. There is also the advantage that the patient himself/herself can easily judge whether or not the data displayed there can be entrusted to the doctor. If there are parts in the displayed graph that you don't like, you can easily delete them and provide them. Although not shown, it is possible to perform an operation such as "partially delete and send" at the time of sending so that the touched part is not sent. In that case, the patient should provide an indication to the physician that that portion of the data was not shared. Although not shown, it goes without saying that the date and time data of data acquisition is also displayed as evidence. Also, as a doctor, even though I can't get any information that will help me make a diagnosis, collecting only the patient's biological information will only become noise, so I will visually check it on the patient's terminal and then ask, "Please give me that information." ”, and may be requested to be sent.
　また、医師が、診断の補助とする以外にも、図９に示すように、医師５３が患者に口頭でアドバイスを与える際に、携帯端末２０の表示部２４にも、エビデンスを表示すると共にアドバイスを表示するようにしてもよい。表示画面２４ｄは、疾患の原因の一例を示しており、図９の例では、「ポリープ」原因の一因が「ストレス」であることを示している。また、表示画面２４ｅは、エビデンスの表示例を示しており、図９に示す例では、図８に示したグラフＧｒ５を表示している。この例では、昨年と最近のストレスが対比して表示されているので、患者にも原因が何であるかが分かり、アドバイスを守ろうという動機付けになる。また、図９において、表示画面２４ｆは、対処法を示しており、この画面には、「クリックでガイドに飛ぶ」と記載されている。患者がこの画面２４ｆをクリックすると、先ほど述べた（１）～（３）の医師からのアドバイスが表示される。また、学習によって生成した推論モデル（後述する図１１参照）を利用して、推論によってアドバイスが表示されるようにしてもよい。 In addition to assisting the diagnosis by the doctor, as shown in FIG. may be displayed. The display screen 24d shows an example of causes of diseases, and in the example of FIG. 9, shows that one of the causes of "polyps" is "stress". Moreover, the display screen 24e shows a display example of evidence, and in the example shown in FIG. 9, the graph Gr5 shown in FIG. 8 is displayed. In this example, last year's stress versus recent stress is displayed, so the patient knows what the cause is and is motivated to follow the advice. Further, in FIG. 9, a display screen 24f shows a countermeasure, and this screen describes "Jump to guide by clicking". When the patient clicks on this screen 24f, the above-mentioned advices (1) to (3) from the doctor are displayed. Advice may also be displayed by inference using an inference model generated by learning (see FIG. 11, which will be described later).
　次に、図１０に示すフローチャートを用いて、ステップＳ３１（図６Ｂ参照）の特定期間の代表値比較でエビデンス判定の動作について説明する。前述したように、ステップＳ３１においては、過去の患者に症状が発生していなかった期間におけるエビデンスデータと、症状が発生している期間におけるエビデンスデータを比較して、症状や疾病等の原因を判定する。 Next, using the flowchart shown in FIG. 10, the operation of evidence determination in the comparison of representative values for a specific period in step S31 (see FIG. 6B) will be described. As described above, in step S31, the evidence data in the past patient's symptom-free period is compared with the evidence data in the symptom-producing period to determine the cause of the symptom, disease, etc. do.
　図１０に示すフローが開始すると、まず、原因候補毎に判定を開始する（Ｓ４１）。図７に示したように、症状（疾病等も含む）の原因の候補は複数あり得るので、この複数ある原因の候補毎に順番に判定を開始する。この順番は、予め症状毎に、判定の順番を決めておいてもよい。また、症状を細かく判定し、この細かく判定した症状に応じて候補を絞ってから判定するようにしてもよい。 When the flow shown in FIG. 10 starts, determination is first started for each cause candidate (S41). As shown in FIG. 7, since there may be a plurality of candidates for the cause of symptoms (including diseases and the like), determination is started for each of the plurality of candidates for causes in order. This order may be determined in advance for each symptom. Alternatively, the symptoms may be determined in detail, and candidates may be narrowed down according to the symptoms determined in detail before determination may be made.
　判定を開始すると、次に、１カ月前の同じ期間と比較する（Ｓ４３）。携帯端末２０は、サーバ１０から依頼のあったエビデンスデータを送信しているので、ここでは、制御部１１は、受信したエビデンスデータの中から、現在と、１カ月前の同じ期間のエビデンスデータを抽出し、比較する。ここで比較する期間としては、例えば、１週間程度等の期間を定めて行えばよく、また症状等の特徴が分かる程度の期間に適宜調整してもよい。また、特定期間（月日（時間）幅））の個々のデータの傾向を比較するのではなく、特定期間における平均値や代表値等を選択するようにしてもよい。 After starting the determination, it is then compared with the same period one month ago (S43). Since the mobile terminal 20 has transmitted the evidence data requested by the server 10, the control unit 11 selects the evidence data for the same period as the present and one month ago from among the received evidence data. Extract and compare. As the period for comparison here, for example, a period of about one week or the like may be determined, and the period may be appropriately adjusted to the extent that characteristics such as symptoms can be recognized. Also, instead of comparing the tendency of individual data in a specific period (month/day (hour) width), an average value, a representative value, or the like in a specific period may be selected.
　次に、悪化方向に差異があるか否かを判定する（Ｓ４５）。ここでは、制御部１１は、原因候補の値が過去に比べて現在の方が悪化しているか否かを判定する。例えば、原因候補がストレスの場合、過去に比べて現在の方が悪化しているか否かを判定する。差異を記憶しておき、最も顕著な差異となったものをエビデンスとしてもよい。 Next, it is determined whether or not there is a difference in the direction of deterioration (S45). Here, the control unit 11 determines whether or not the value of the candidate cause is worse now than in the past. For example, if the cause candidate is stress, it is determined whether or not it is worse now than in the past. The differences may be stored and the most significant difference may be used as evidence.
　ステップＳ４５における判定の結果、悪化方向に差異がある場合には、その原因候補をエビデンスの候補にする（Ｓ４７）。原因候補が過去に比べて悪化してきている場合には、その原因候補が、患者の疾患等の原因となっている可能性があることから、エビデンス候補にする。 As a result of the determination in step S45, if there is a difference in the direction of deterioration, the cause candidate is used as evidence candidate (S47). If the cause candidate is worsening than in the past, it is regarded as an evidence candidate because the cause candidate may be the cause of the patient's disease or the like.
　ステップＳ４７においてエビデンス候補にすると、またはステップＳ４５における判定の結果、悪化方向に差異がない場合には、次に、１年前まで確認したか否かを判定する（Ｓ４９）。ステップＳ４３において、１カ月前のエビデンスデータと現在のエビデンスデータを比較した後、ステップＳ５１において更に１か月前に比較対象を変更し、１カ月ずつ過去に遡って比較を行っている。このステップでは、１年前まで比較対象が遡ったか否かを判定する。 If an evidence candidate is selected in step S47, or if the result of determination in step S45 is that there is no difference in the direction of deterioration, then it is determined whether or not confirmation has been made up to one year ago (S49). In step S43, after comparing the evidence data of one month ago with the present evidence data, in step S51, the object of comparison is changed to one month before, and the comparison is performed one month at a time. In this step, it is determined whether or not the comparison target goes back one year.
　ステップＳ４９における判定の結果、１年前まで確認していない場合には、更に比較対象を１か月前に遡る（Ｓ５１）。比較対象をさらに１か月前に遡ると、ステップＳ４１に戻り、前述の処理を繰り返す。なお、ステップＳ４９における１年前、またステップＳ５１における１か月前は、例示であり、症状（疾患）等の特性に合わせて、この数値は適宜変更してもよい。 As a result of the determination in step S49, if confirmation has not been made up to one year ago, the comparison target is further traced back one month (S51). If the comparison target is further traced back one month, the process returns to step S41 and the above-described processing is repeated. Note that one year before in step S49 and one month before in step S51 are examples, and these numerical values may be appropriately changed according to characteristics such as symptoms (disease).
　ステップＳ４９における判定の結果、１年前まで確認が済んでいる場合には、次に、全原因候補について確認が済んだか否かを判定する（Ｓ５３）。ステップＳ４１において、想定される原因候補を挙げ、この原因候補毎に判定を開始いる。ここでは、制御部１１が、挙げられている全原因候補について、ステップＳ４１～Ｓ５１における処理を実行したか否かを判定する。 If the result of determination in step S49 is that confirmation has been completed up to one year ago, it is next determined whether or not all candidate causes have been confirmed (S53). In step S41, possible cause candidates are listed, and determination is started for each cause candidate. Here, the control unit 11 determines whether or not the processes in steps S41 to S51 have been executed for all of the listed cause candidates.
　ステップＳ５３における判定の結果、全原因候補について確認が終わっていない場合には、別候補で判定する（Ｓ５５）。ここでは、制御部１１が、判定が終了していない原因候補について、順番に従って、次の原因候補を決める。次に判定を行う原因候補を決めると、ステップＳ４１に戻る。 If the result of determination in step S53 is that confirmation has not been completed for all candidate causes, another candidate is determined (S55). Here, the control unit 11 determines the next cause candidate according to the order of the cause candidates for which determination has not been completed. After determining the cause candidates to be determined next, the process returns to step S41.
　ステップＳ５３における判定の結果、全原因候補について確認が終わっている場合には、次に、候補化されたものから判定を行う（Ｓ５７）。ステップＳ４７において、エビデンスデータが悪化してものをエビデンス候補としている。ここでは、制御部１１が候補として抽出したエビデンスの中から、症状（疾患）の原因となったものを選び出す。原因候補の決定方法としては、それぞれの症状（疾患）の特性に応じて行うが、例えば、エビデンスデータの悪化の度合いが大きいものを選択するようにしてもよい。ステップＳ５７の処理が終わると、特定期間の代表値比較でエビデンス判定のフローを終了し、元のフローに戻る。 If the result of determination in step S53 is that confirmation has been completed for all candidate causes, then determination is made starting with the candidates (S57). In step S47, evidence data deteriorated are taken as evidence candidates. Here, the evidence that caused the symptom (disease) is selected from the evidence extracted as candidates by the control unit 11 . The cause candidates are determined according to the characteristics of each symptom (disease), and, for example, a cause with a large degree of deterioration in evidence data may be selected. When the process of step S57 is completed, the flow of evidence determination is terminated by the comparison of the representative values for the specific period, and the original flow is returned to.
　このように、図１０に示すエビデンス判定のフローにおいては、原因候補について、現在と過去の特定期間におけるエビデンスデータを比較し、このエビデンスデータが悪化している否かを、比較対象の期間を変えながら判定している。また、複数の原因候補がある場合には、個々の原因候補について、同様に、比較判定を行っている。これらの処理を行うことによって、患者の症状が悪化した原因を突き止めることができ、適切な処方を施すことができる。 As described above, in the evidence determination flow shown in FIG. 10, the evidence data for the cause candidate in the current and past specific periods are compared, and whether or not the evidence data is deteriorating is determined by changing the period to be compared. I am judging. In addition, when there are a plurality of cause candidates, each cause candidate is similarly compared and determined. By performing these treatments, it is possible to find out the cause of worsening of the patient's symptoms, and to give an appropriate prescription.
　また、エビデンス判定のフローにおいては、医師の問診または診断に従って選択された候補となる原因に基づいて、第１および第２の生体情報パターンの種類を選択し、この選択された種類の第１及び第２の生体情報パターンについて差異を調べている。差異が見つかれば、このエビデンスデータの種類に基づいて、疾患の原因が分かるので、適切な処方を行うことができる。 Further, in the evidence determination flow, based on the candidate cause selected according to the doctor's interview or diagnosis, the first and second biological information pattern types are selected, and the selected types of the first and second Differences are examined for the second biometric pattern. If a difference is found, the cause of the disease can be determined based on the type of evidence data, and appropriate prescription can be made.
　次に、図１１を用いて、ステップＳ３５（図６Ｂ参照）における推論モデルの生成について説明する。サーバ１０には、多数の携帯端末２０からユーザのエビデンスデータが集積される。この多数のエビデンスデータを用いて、症状の推論用や治療用の推論モデルを生成することができる。 Next, generation of an inference model in step S35 (see FIG. 6B) will be described using FIG. User's evidence data is accumulated in the server 10 from many mobile terminals 20 . This large body of evidence data can be used to generate inference models for symptom inference and treatment.
　図１１の上段には、ファイル管理されているエビデンスデータの例を示す。グラフＧｒ５は、胃部内視鏡検査でポリープ切除し、食事制限を行ったユーザのストレスデータを示す。この例において、昨年のストレス信号ＳｉｇＬＹと最近のストレス信号ＳｉｇＣの変化を示している。同様に、グラフＧｒ６は、大腸内視鏡検査で再検査となったユーザのデータを示している。この例においても、昨年と最近の睡眠時間の変化を示している。これらの例では、１年前等と規格化して、類似データを集めているので、データを収集しやすく、また比較が容易となる。また、季節差の影響を少なくなる。 The upper part of Fig. 11 shows an example of file-managed evidence data. Graph Gr5 shows the stress data of a user who had a polyp removed by gastric endoscopy and underwent dietary restrictions. In this example, the change of the stress signal SigLY of the last year and the stress signal of the recent SigC is shown. Similarly, graph Gr6 shows the data of the user who was reexamined in the colonoscopy. This example also shows the change in sleep hours between last year and recent years. In these examples, since similar data are collected by standardizing them as one year ago or the like, the data can be easily collected and compared. In addition, the influence of seasonal differences is reduced.
　このようなグラフは、患者の使用する端末に表示可能としてもよい。グラフによって表示されれば、単にデータの集まりを見せられるより、具体的なイメージが把握しやすく、医師がその表示を見て診断の一助にしやすい。ただし、患者によって、全データが共有されなかった場合、その旨の表示を医師に伝えるようにする。図示していないが、データ取得の日時データなどもエビデンスとして表示することは言うまでもない。 Such graphs may be displayed on the terminal used by the patient. When displayed as a graph, it is easier for a doctor to grasp a specific image rather than simply showing a collection of data, and for a doctor to look at the display and help with diagnosis. However, if the patient did not share all the data, an indication to that effect should be communicated to the physician. Although not shown, it goes without saying that the date and time data of data acquisition is also displayed as evidence.
　また、グラフＧｒ５、Ｇｒ６においては、横軸を年齢・性別等としているが、これに限らず、症状等の原因に影響し易い因子を入れて、原因解析を行うとよい。例えば、遺伝子情報を取り入れてもよい。 In addition, in the graphs Gr5 and Gr6, the horizontal axis is age, gender, etc., but it is not limited to this, and it is better to include factors that are likely to affect the causes of symptoms and perform cause analysis. For example, genetic information may be incorporated.
　図１１の下段には、エビデンスデータを用いて学習を行い、推論モデルを生成するための構成を示す。グラフＧｒ５、Ｇｒ６に示した個々のエビデンスデータに対して、症状や治療情報（例えば、Ｇｒ５の胃部内視鏡検査でポリープ切除、食事制限や、Ｇｒ６の大腸内視鏡検査で再検査）をアノテーションして、教師データを作成する。教師データが作成されると、エビデンスデータをニューラル・ネットワークの入力層Ｉｎに入力し、出力層Ｏｕｔに症状や治療情報が出力されるように、深層学習を行い、中間層の各層の重み付けを算出し、推論モデルを生成する。 The lower part of FIG. 11 shows the configuration for learning using evidence data and generating an inference model. For the individual evidence data shown in graphs Gr5 and Gr6, symptoms and treatment information (for example, polypectomy and dietary restrictions in Gr5 gastric endoscopy, and reexamination in Gr6 colonoscopy) are provided. Create teacher data by annotating. When the teacher data is created, the evidence data is input to the input layer In of the neural network, deep learning is performed so that the symptom and treatment information is output to the output layer Out, and the weighting of each intermediate layer is calculated. and generate an inference model.
　推論モデルが生成されると、例えば、ステップＳ５（図６Ａ参照）において、推論を行うことができ、またＳ１３において、改善アドバイスや服薬アドバイスを行うことが可能となる。例えば、ポリープが発生している可能性のあるユーザに対して、「内視鏡受診をお勧めします（このようなエビデンスの有る方は処置を行っています）」等のアドバイスを行うことができる。なお、図１１では、生体情報に基づいて教師データを作成し、推論モデルを生成する例について説明したが、これに限らず、生活習慣情報に基づいて、健康アドバイスを行うための推論モデルを生成してもよく、また生体情報および生活習慣情報の両方を用いて、推論モデルを生成するようにしてもよい。 When the inference model is generated, for example, inference can be made in step S5 (see FIG. 6A), and improvement advice and medication advice can be given in S13. For example, for a user who may have polyps, it is possible to give advice such as "I recommend you to have an endoscopy (if you have such evidence, we are taking treatment)". can. Although FIG. 11 illustrates an example in which teacher data is created based on biometric information and an inference model is generated, the present invention is not limited to this, and an inference model for giving health advice is generated based on lifestyle information. Alternatively, an inference model may be generated using both biometric information and lifestyle information.
　ここで、深層学習について、説明する。「深層学習（ディープ・ラーニング）」は、ニューラル・ネットワークを用いた「機械学習」の過程を多層構造化したものである。情報を前から後ろに送って判定を行う「順伝搬型ニューラル・ネットワーク」が代表的なものである。順伝搬型ニューラル・ネットワークは、最も単純なものでは、Ｎ１個のニューロンで構成される入力層、パラメータで与えられるＮ２個のニューロンで構成される中間層、判別するクラスの数に対応するＮ３個のニューロンで構成される出力層の３層があればよい。入力層と中間層、中間層と出力層の各ニューロンはそれぞれが結合加重で結ばれ、中間層と出力層はバイアス値が加えられることによって、論理ゲートを容易に形成できる。 Here, deep learning will be explained. "Deep learning" is a multilayer structure of the process of "machine learning" using neural networks. A typical example is a "forward propagation neural network" that sends information from front to back and makes decisions. The simplest forward propagation neural network consists of an input layer composed of N1 neurons, an intermediate layer composed of N2 neurons given by parameters, and N3 neurons corresponding to the number of classes to be discriminated. It suffices if there are three output layers composed of neurons. The neurons of the input layer and the intermediate layer, and the intermediate layer and the output layer are connected by connection weights, respectively, and the intermediate layer and the output layer are added with bias values, so that logic gates can be easily formed.
　ニューラル・ネットワークは、簡単な判別を行うのであれば３層でもよいが、中間層を多数にすることによって、機械学習の過程において複数の特徴量の組み合わせ方を学習することも可能となる。近年では、９層～１５２層のものが、学習にかかる時間や判定精度、消費エネルギーの観点から実用的になっている。また、画像の特徴量を圧縮する、「畳み込み」と呼ばれる処理を行い、最小限の処理で動作し、パターン認識に強い「畳み込み型ニューラル・ネットワーク」を利用してもよい。また、より複雑な情報を扱え、順番や順序によって意味合いが変わる情報分析に対応して、情報を双方向に流れる「再帰型ニューラル・ネットワーク」（全結合リカレントニューラルネット）を利用してもよい。 The neural network may have three layers for simple discrimination, but by increasing the number of intermediate layers, it is also possible to learn how to combine multiple feature values in the process of machine learning. In recent years, 9 to 152 layers have become practical from the viewpoint of the time required for learning, judgment accuracy, and energy consumption. In addition, a process called "convolution" that compresses the feature amount of an image may be performed, and a "convolution neural network" that operates with minimal processing and is strong in pattern recognition may be used. In addition, a "recurrent neural network" (fully-connected recurrent neural network), which can handle more complicated information and can handle information analysis whose meaning changes depending on the order and order, may be used in which information flows in both directions.
　これらの技術を実現するために、ＣＰＵやＦＰＧＡ（Field Programmable Gate Array）等の従来からある汎用的な演算処理回路を使用してもよい。しかし、これに限らず、ニューラル・ネットワークの処理の多くが行列の掛け算であることから、行列計算に特化したＧＰＵ（Graphic Processing Unit）やTensor Processing Unit（TPU）と呼ばれるプロセッサを利用してもよい。近年ではこのような人工知能（ＡＩ）専用ハードの「ニューラル・ネットワーク・プロセッシング・ユニット（ＮＰＵ）」がＣＰＵ等その他の回路とともに集積して組み込み可能に設計され、処理回路の一部になっている場合もある。 In order to realize these technologies, conventional general-purpose arithmetic processing circuits such as CPUs and FPGAs (Field Programmable Gate Arrays) may be used. However, not only this, but since most neural network processing is matrix multiplication, it is also possible to use processors called GPUs (Graphic Processing Units) and Tensor Processing Units (TPUs) that specialize in matrix calculations. good. In recent years, such artificial intelligence (AI) dedicated hardware "neural network processing unit (NPU)" is designed to be integrated and embedded with other circuits such as CPU, and has become a part of the processing circuit. In some cases.
　その他、機械学習の方法としては、例えば、サポートベクトルマシン、サポートベクトル回帰という手法もある。ここでの学習は、識別器の重み、フィルタ係数、オフセットを算出するものあり、これ以外にも、ロジスティック回帰処理を利用する手法もある。機械に何かを判定させる場合、人間が機械に判定の仕方を教える必要がある。本実施形態においては、画像の判定を、機械学習によって導出する手法を採用したが、そのほか、人間が経験則・ヒューリスティクスによって獲得したルールを適応するルールベースの手法を用いてもよい。 In addition, there are other methods of machine learning, such as support vector machines and support vector regression. The learning here involves calculation of classifier weights, filter coefficients, and offsets, and there is also a method using logistic regression processing. If you want a machine to judge something, you have to teach the machine how to judge. In the present embodiment, a method of deriving image determination by machine learning is used. In addition, a rule-based method that applies rules acquired by humans through empirical rules and heuristics may be used.
　次に、図１２を用いて、消化器系の危険因子と、ストレスおよび生活習慣について説明する。消化器の病気の危険因子はストレスであり、また生活習慣である。図１２の左欄に、消化器の病気として、胃・食道の病気の危険因子を示している。この危険因子としては、ストレスと生活習慣がある。この生活習慣としては、油っこい食事、甘いもの、熱すぎるものや、カフェイン・香辛料等の刺激物、多量の飲酒、喫煙、ピロリ菌がある。 Next, using Fig. 12, digestive system risk factors, stress, and lifestyle habits will be explained. Risk factors for gastrointestinal disease are stress and lifestyle. The left column of FIG. 12 shows the risk factors for gastric and esophageal diseases as digestive diseases. These risk factors include stress and lifestyle. These lifestyle habits include fatty foods, sweet foods, too hot foods, stimulants such as caffeine and spices, excessive drinking, smoking, and Helicobacter pylori.
　危険因子としてのストレスは、生活上の出来事の影響を受ける。本実施形態においては、生体情報に基づいてストレスを判断したが、生体情報以外にも、生活上の出来事（イベント）に基づいてストレスを判定する方法も知られている。図１２の右上欄には、生活上の出来事に対するストレス度を示している。例えば、配偶者の死亡はストレス度が１００であり、離婚はストレス度が７３であり、別居はストレス度が６６である。生活上の出来事（イベント）等は、携帯端末２０に記録された行動履歴や消費行動やＳＮＳ、写真等に基づいて判定することができる。また、携帯端末２０に限らず、ユーザ等がサーバ３０に投稿した情報等に基づいて判定してもよい。 Stress as a risk factor is affected by life events. In the present embodiment, stress is determined based on biometric information, but methods other than biometric information for determining stress based on life events are also known. The upper right column of FIG. 12 shows the degree of stress for events in life. For example, the death of a spouse has a stress level of 100, divorce has a stress level of 73, and separation has a stress level of 66. Life events (events) can be determined based on the action history, consumption behavior, SNS, photos, etc. recorded in the mobile terminal 20 . Further, determination may be made based on information or the like posted to the server 30 by a user or the like, not limited to the mobile terminal 20 .
　また、前述したように生活習慣が病気の要因になったりする。図１２の右下欄に、生活習慣等とがん発生の関係を示している。このがん発生要因となる生活習慣としては、例えば、喫煙（能動）、感染性要因（例えば、ピロリ菌）、飲酒、塩分摂取、運動不足等が挙げられる。これらの生活習慣は、携帯端末２０やサーバ３０等に記録された、行動履歴や消費行動やＳＮＳ、写真等に基づいて判定することができる。体重等は、携帯端末２０と連携した体重計からデータを取得してもよい。また、電子カルテ情報や処方箋情報が医師等と患者の間でシェアしている場合には、これらの情報に基づいて、生活習慣情報や生体情報を取得しても良い。また、前述のピロリ菌等の感染症を検査する検査キットは、インターネット等を通じて行う電子取引によって一般ユーザが購入できることから、これらの購入履歴や、また検査キットを用いた診断サービス等の履歴から、生活習慣情報等を取得しても良い。 Also, as mentioned above, lifestyle habits can be a factor in illness. The lower right column of FIG. 12 shows the relationship between lifestyle habits and the occurrence of cancer. Examples of lifestyle habits that cause cancer include smoking (active), infectious factors (eg, Helicobacter pylori), alcohol consumption, salt intake, lack of exercise, and the like. These lifestyle habits can be determined based on the action history, consumption behavior, SNS, photos, etc. recorded in the mobile terminal 20, the server 30, and the like. The weight and the like may be acquired from a weight scale linked with the mobile terminal 20 . Moreover, when electronic medical record information and prescription information are shared between doctors and the like, lifestyle information and biometric information may be acquired based on these information. In addition, since the test kits for testing infectious diseases such as Helicobacter pylori mentioned above can be purchased by general users through electronic transactions conducted through the Internet, etc., based on the purchase history of these and the history of diagnostic services using test kits, etc. Lifestyle information and the like may be acquired.
　図１２に示すように、胃・食道の病気の危険因子は、ストレスや生活習慣と関係がある。本実施形態においては、医師が診察した際に、患者の疾患等の原因となったエビデンスデータを、患者の携帯端末２０から収集し、その原因を探求している（図６ＢのＳ２５～Ｓ３３参照）。そこで、収集した生体情報に加えて、生活習慣情報を用いて、ユーザに傾向に関するアドバイスを行うことのできる推論モデルを生成することができる（例えば、図６ＢのＳ３５、図１１参照）。 As shown in Figure 12, risk factors for stomach and esophageal diseases are related to stress and lifestyle habits. In this embodiment, when a doctor examines a patient, the evidence data that caused the patient's disease or the like is collected from the patient's mobile terminal 20, and the cause is explored (see S25 to S33 in FIG. 6B). ). Therefore, in addition to the collected biometric information, lifestyle information can be used to generate an inference model capable of giving advice to the user regarding tendencies (see, for example, S35 in FIG. 6B and FIG. 11).
　以上説明したように、本発明の一実施形態における情報処理方法は、第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンを取得する取得ステップ（例えば、図６ＢのＳ２９）と、第１の生体情報パターンと第２の生体情報パターンを比較し、この比較に基づいて、患者のストレスを判定するストレス判定ステップを有している。このように、患者の第１および第２の特定期間における生体情報パターンを収集し、この２つのパターンを比較することによって、ストレスがあるか否かを判定している。このため、患者のストレスを容易に判定することができる。 As described above, the information processing method according to one embodiment of the present invention acquires a first biological information pattern in a specific period going back a first time and a second biological information pattern going back a specific period of time going back a second time. and a stress determination step of comparing the first biometric information pattern and the second biometric information pattern and determining the stress of the patient based on this comparison. . In this way, it is determined whether or not there is stress by collecting biometric information patterns in the first and second specific periods of the patient and comparing the two patterns. Therefore, the patient's stress can be easily determined.
　また、本発明の一実施形態における情報処理方法は、医師の問診または診断に従って、疾患の原因となる候補を検索し、この原因候補を検証するためのエビデンスデータの送付を患者の携帯端末に要求する要求ステップ（例えば、図６ＢのＳ２７、Ｓ２９参照）と、携帯端末からエビデンスデータを受信すると、該エビデンスデータについての、第１の時間遡る特定期間における第１のパターンと、第２の時間遡る特定期間における第２のパターンを比較する比較ステップ（例えば、図６ＢのＳ３１参照）と、第１のパターンと第２のパターンの比較結果に基づいて、原因候補の中から患者の疾患の原因を決定し、患者に処方する処方ステップ（例えば、図６ＢのＳ３５参照）を有している。このように、疾患の原因となる候補を検証するためのエビデンスデータを患者の携帯端末から収集し、検証しているので、効率的にエビデンスデータを収集することができる。また、このエビデンスデータに基づいて、精度の高い処方を行うことができる。 In addition, the information processing method according to one embodiment of the present invention searches for a candidate causing a disease according to an interview or diagnosis by a doctor, and requests the patient's portable terminal to send evidence data for verifying this candidate cause. request step (see, for example, S27 and S29 in FIG. 6B), and when the evidence data is received from the mobile terminal, a first pattern in a specific period going back a first time and a second time going back for the evidence data A comparison step of comparing a second pattern in a specific period (see, for example, S31 in FIG. 6B); It has a prescribing step (see, for example, S35 in FIG. 6B) of determining and prescribing to the patient. In this way, since the evidence data for verifying the candidate causing the disease is collected from the patient's portable terminal and verified, the evidence data can be efficiently collected. Moreover, highly accurate prescription can be performed based on this evidence data.
　また、本発明の一実施形態における携帯端末の情報共有方法は、携帯端末が依頼信号を受信する受信ステップ（例えば、図６ＡのＳ７参照）と、　依頼信号に応じて、携帯端末の記録部に記録された情報の中から依頼に応じた情報を検索する検索ステップ（例えば、図６ＡのＳ７参照）と、検索された情報を携帯端末の表示部に表示すると共に、表示された情報の依頼元に送信を許可するか否かのスイッチ表示を行う表示制御ステップ（例えば、図６ＡのＳ７、Ｓ８参照）を有している。上述の依頼信号としては、医師等と繋がっているサーバ１０からの医療に関する情報の提供を依頼する信号である。医療に関する情報としては、例えば、図７に示されるようなエビデンスデータ、生体情報、生活習慣情報等が含まれる。本実施形態においては、サーバ１０から端末情報を提供することの依頼を受けても、直ちに送信するのではなく、検索された情報を表示し、さらに依頼元（例えば、サーバ１０）に、提供者（携帯端末の所有者等）が医療に関する情報を提供するか否かを決定することができる。このため、携帯端末の所有者等が、医療情報のような個人情報を外部に提供することをコントロールすることができる。 Further, the information sharing method of the mobile terminal according to one embodiment of the present invention includes a receiving step (for example, see S7 in FIG. 6A) in which the mobile terminal receives a request signal, and a recording unit of the mobile terminal in response to the request signal A search step (see, for example, S7 in FIG. 6A) for searching for requested information from among the recorded information, displaying the searched information on the display unit of the mobile terminal, and identifying the requester of the displayed information It has a display control step (see, for example, S7 and S8 in FIG. 6A) for displaying a switch indicating whether or not to permit transmission. The above request signal is a signal requesting provision of medical information from the server 10 connected to a doctor or the like. The medical information includes, for example, evidence data as shown in FIG. 7, biological information, lifestyle information, and the like. In this embodiment, even if a request to provide terminal information is received from the server 10, the retrieved information is displayed instead of being immediately transmitted, and furthermore, the requester (for example, the server 10) is informed that the provider A person (such as the owner of the mobile device) can decide whether or not to provide medical information. Therefore, the owner of the mobile terminal or the like can control the provision of personal information such as medical information to the outside.
　なお、本発明の一実施形態においては、生体情報および生活習慣に関するエビデンスデータを取得していたが、いずれか一方でもよい。また、生体情報および生活習慣に関するエビデンスデータを取得し、両エビデンスデータを用いて判定してもよい。 It should be noted that in one embodiment of the present invention, biometric information and evidence data on lifestyle habits were acquired, but either one may be acquired. Also, biometric information and evidence data relating to lifestyle habits may be obtained and both evidence data may be used for determination.
　また、本発明の一実施形態においては、サーバ１０は１つのサーバとして説明したが、２以上のサーバであってもよく、また、複数のサーバが連携するようにしてもよい。さらに、外部のサーバ３０と一体に構成してもよい。また、本発明の一実施形態においては、制御部１１、２１は、ＣＰＵやメモリ等から構成されている機器として説明した。しかし、ＣＰＵとプログラムによってソフトウエア的に構成する以外にも、各部の一部または全部をハードウエア回路で構成してもよく、ヴェリログ（Verilog）やＶＨＤＬ（Verilog Hardware Description Language）等によって記述されたプログラム言語に基づいて生成されたゲート回路等のハードウエア構成でもよく、またＤＳＰ（Digital Signal Processor）等のソフトを利用したハードウエア構成を利用してもよい。これらは適宜組み合わせてもよいことは勿論である。 Also, in one embodiment of the present invention, the server 10 is explained as one server, but it may be two or more servers, and a plurality of servers may cooperate. Furthermore, it may be configured integrally with the external server 30 . Also, in one embodiment of the present invention, the controllers 11 and 21 have been described as devices configured from CPUs, memories, and the like. However, in addition to being configured as software by a CPU and a program, part or all of each part may be configured as a hardware circuit, and is described in Verilog, VHDL (Verilog Hardware Description Language), etc. A hardware configuration such as a gate circuit generated based on a program language may be used, or a hardware configuration using software such as a DSP (Digital Signal Processor) may be used. Of course, these may be combined as appropriate.
　また、制御部１１、２１は、ＣＰＵに限らず、コントローラとしての機能を果たす素子であればよく、上述した各部の処理は、ハードウエアとして構成された１つ以上のプロセッサが行ってもよい。例えば、各部は、それぞれが電子回路として構成されたプロセッサであっても構わないし、ＦＰＧＡ（Field Programmable Gate Array）等の集積回路で構成されたプロセッサにおける各回路部であってもよい。または、１つ以上のＣＰＵで構成されるプロセッサが、記録媒体に記録されたコンピュータプログラムを読み込んで実行することによって、各部としての機能を実行しても構わない。 In addition, the control units 11 and 21 are not limited to CPUs, and may be elements that function as controllers, and the processing of each unit described above may be performed by one or more processors configured as hardware. For example, each unit may be a processor configured as an electronic circuit, or may be each circuit unit in a processor configured with an integrated circuit such as an FPGA (Field Programmable Gate Array). Alternatively, a processor composed of one or more CPUs may read and execute a computer program recorded on a recording medium, thereby executing the function of each unit.
　また、本発明の一実施形態においては、サーバ１０は、制御部１１、通信部１２、問診入力部１３、ＤＢ検索部１４、時計部１５、生体情報比較部１６、ストレス判定部１７、生活習慣判定部１８、記録・ＤＢ部１９を有しているものとして説明した。しかし、これらは一体の装置内に設けられている必要はなく、例えば、インターネット等の通信網によって接続されていれば、上述の各部は分散されていても構わない。また、上述の各部の機能の一部は、携帯端末２０および／またはサーバ３０に分散して配置してもよい。 In one embodiment of the present invention, the server 10 includes a control unit 11, a communication unit 12, an inquiry input unit 13, a DB search unit 14, a clock unit 15, a biological information comparison unit 16, a stress determination unit 17, a lifestyle It has been described as having the determination unit 18 and the recording/DB unit 19 . However, they do not need to be provided in an integrated device, and the above-described units may be distributed as long as they are connected by a communication network such as the Internet. Also, some of the functions of the respective units described above may be distributed and arranged in the mobile terminal 20 and/or the server 30 .
　また、本発明の一実施形態においては、携帯端末２０は、制御部２１、通信部２２、入力部２３、表示部２４、時計部２５、生体情報取得部２６、生活情報取得部２７、アドバイス部２８、記録部２９を有しているものとして説明した。しかし、これらは一体の装置内に設けられている必要はなく、例えば、インターネット等の通信網によって接続されていれば、上述の各部は分散されていても構わない。また、上述の各部の機能の一部は、サーバ１０および／またはサーバ３０に分散して配置してもよい。 In one embodiment of the present invention, the mobile terminal 20 includes a control unit 21, a communication unit 22, an input unit 23, a display unit 24, a clock unit 25, a biological information acquisition unit 26, a life information acquisition unit 27, an advice unit 28 and a recording unit 29 are described. However, they do not need to be provided in an integrated device, and the above-described units may be distributed as long as they are connected by a communication network such as the Internet. Also, some of the functions of the respective units described above may be distributed and arranged in the server 10 and/or the server 30 .
　また、近年は、様々な判断基準を一括して判定できるような人工知能が用いられる事が多く、ここで示したフローチャートの各分岐などを一括して行うような改良もまた、本発明の範疇に入るものであることは言うまでもない。そうした制御に対して、ユーザが善し悪しを入力可能であれば、ユーザの嗜好を学習して、そのユーザにふさわしい方向に、本願で示した実施形態はカスタマイズすることが可能である。 In addition, in recent years, artificial intelligence that can collectively determine various judgment criteria is often used, and improvements such as collectively performing each branch of the flow chart shown here are also within the scope of the present invention. It goes without saying that the If the user can input good or bad for such control, it is possible to learn the user's preference and customize the embodiment shown in the present application in a direction suitable for the user.
　また、本明細書において説明した技術のうち、主にフローチャートで説明した制御に関しては、プログラムで設定可能であることが多く、記録媒体や記録部に収められる場合もある。この記録媒体、記録部への記録の仕方は、製品出荷時に記録してもよく、配布された記録媒体を利用してもよく、インターネットを通じてダウンロードしたものでもよい。 In addition, among the techniques described in this specification, the control described mainly in the flowcharts can often be set by a program, and may be stored in a recording medium or recording unit. The method of recording in the recording medium and the recording unit may be recorded at the time of product shipment, using a distributed recording medium, or downloading via the Internet.
　また、本発明の一実施形態においては、フローチャートを用いて、本実施形態における動作を説明したが、処理手順は、順番を変えてもよく、また、いずれかのステップを省略してもよく、ステップを追加してもよく、さらに各ステップ内における具体的な処理内容を変更してもよい。 In addition, in one embodiment of the present invention, the operation in this embodiment was explained using a flowchart, but the order of the processing procedure may be changed, or any step may be omitted. Steps may be added, and specific processing contents within each step may be changed.
　また、特許請求の範囲、明細書、および図面中の動作フローに関して、便宜上「まず」、「次に」等の順番を表現する言葉を用いて説明したとしても、特に説明していない箇所では、この順で実施することが必須であることを意味するものではない。 In addition, even if the operation flow in the claims, the specification, and the drawings is explained using words expressing the order such as "first" and "next" for convenience, in places not specifically explained, It does not mean that it is essential to carry out in this order.
　本発明は、上記実施形態にそのまま限定されるものではなく、実施段階ではその要旨を逸脱しない範囲で構成要素を変形して具体化できる。また、上記実施形態に開示されている複数の構成要素の適宜な組み合わせによって、種々の発明を形成できる。例えば、実施形態に示される全構成要素の幾つかの構成要素を削除してもよい。さらに、異なる実施形態にわたる構成要素を適宜組み合わせてもよい。 The present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the spirit of the present invention at the implementation stage. Also, various inventions can be formed by appropriate combinations of the plurality of constituent elements disclosed in the above embodiments. For example, some components of all components shown in the embodiments may be omitted. Furthermore, components across different embodiments may be combined as appropriate.
１０・・・サーバ、１１・・・制御部、１２・・・通信部、１３・・・問診入力部、１４・・・ＤＢ検索部、１５・・・時計部、１６・・・生体情報比較部、１７・・・ストレス判定部、１８・・・生活習慣判定部、１９・・・記録部・ＤＢ部、１９ａ・・・プロフィール毎の診察記録部、１９ｃ・・・プロフィール毎の生体情報、１９ｄ・・・疾病別原因、２０・・・携帯端末、２１・・・制御部、２２・・・通信部、２３・・・入力部、２４・・・表示部、２５・・・時計部、２６・・・生体情報取得部、２７・・・生活情報取得部、２８・・・アドバイス部、２９・・・記録部 10... server, 11... control unit, 12... communication unit, 13... inquiry input unit, 14... DB search unit, 15... clock unit, 16... biometric information comparison Section 17 Stress determination section 18 Lifestyle habit determination section 19 Recording section/DB section 19a Medical examination recording section for each profile 19c Biological information for each profile 19d... Cause by disease, 20... Portable terminal, 21... Control unit, 22... Communication unit, 23... Input unit, 24... Display unit, 25... Clock unit, 26... Biological information acquisition unit, 27... Life information acquisition unit, 28... Advice unit, 29... Recording unit

Claims (21)

  1. 　第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンを取得する取得ステップと、
    　上記第１の生体情報パターンと第２の生体情報パターンを比較し、この比較に基づいて、患者のストレスを判定するストレス判定ステップと、
    　を有することを特徴とする情報収集方法。     an acquiring step of acquiring a first biometric information pattern for a specific period going back a first time and a second biometric information pattern for a specific period going back a second time;
    a stress determination step of comparing the first biological information pattern and the second biological information pattern and determining the patient's stress based on the comparison;
    An information gathering method characterized by having
  2. 　上記患者の現在の病状情報に対して、上記第１の生体情報パターンと、上記第２の生体情報パターンを関連づけて記録する記録ステップを、　さらに具備することを特徴とする請求項１に記載の情報収集方法。 2. The method according to claim 1, further comprising a recording step of recording the first biological information pattern and the second biological information pattern in association with current medical condition information of the patient. How we collect information.
  3. 　上記第１および第２の生体情報パターンの取得に先立って、上記患者の有する携帯端末に対して上記第１および第２の生体情報パターンを取得する旨を通知する通信ステップを具備することを特徴とする請求項１に記載の情報収集方法。 characterized by comprising a communication step of notifying the mobile terminal of the patient that the first and second biometric information patterns are to be acquired, prior to acquisition of the first and second biometric information patterns. The information collection method according to claim 1, wherein:
  4. 　上記特定期間において、上記第１および第２の生体情報パターンの代表値を比較することによって行う原因エビデンス判定は、候補となる原因毎に上記遡った時期における上記第１および第２の生体情報パターンの差異を調べることを特徴とする請求項１に記載の情報収集方法。 In the specific period, the cause evidence determination performed by comparing the representative values of the first and second biological information patterns is based on the first and second biological information patterns in the retroactive period for each candidate cause. 2. The method of collecting information according to claim 1, wherein the difference between is investigated.
  5. 　上記原因エビデンス判定において、上記遡る時期は、候補となる原因の差異を調べ、差異が明確な時期とすることを特徴とする請求項４に記載の情報収集方法。 The information gathering method according to claim 4, wherein in the cause evidence determination, the retroactive time is set to a time when differences in candidate causes are investigated and the differences are clear.
  6. 　医師の問診または診断に従って選択された候補となる原因に基づいて、上記第１および第２の生体情報パターンの種類を選択し、この選択された種類の上記第１及び第２の生体情報パターンについて上記差異を調べることを特徴とする請求項１に記載の情報収集方法。 selecting the types of the first and second biological information patterns based on the candidate causes selected according to the doctor's inquiry or diagnosis; and determining the selected types of the first and second biological information patterns 2. The method of collecting information according to claim 1, wherein the differences are examined.
  7. 　上記ストレスの判定結果に基づいて、上記患者への治療方法を決定することを特徴とする請求項１に記載の情報収集方法。 The information gathering method according to claim 1, wherein a treatment method for the patient is determined based on the stress determination result.
  8. 　上記第１及び第２の生体情報パターンは、心電図のＲ波最大値時点間の 間隔を、直前に求められたものと比較可能なパターンであることを特徴とする請求項１に記載の情報収集方法。 2. The information gathering according to claim 1, wherein the first and second biological information patterns are patterns in which the interval between the R-wave maximum values of an electrocardiogram can be compared with the immediately preceding one. Method.
  9. 　上記第１および第２の生体情報パターンは、一日の血圧変化のピーク値と回数が比較可能なパターンであることを特徴とする請求項１に記載の情報収集方法。 　The information gathering method according to claim 1, wherein the first and second biological information patterns are patterns with which peak values and times of blood pressure changes in a day can be compared.
  10. 　上記ストレスの判定は、上記第１および第２の生体情報パターンの揺らぎを比較することによって行うことを特徴とする請求項１に記載の情報収集方法。 The information gathering method according to claim 1, wherein the determination of the stress is performed by comparing fluctuations of the first and second biometric information patterns.
  11. 　第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンを取得する取得部と、
    　上記第１の生体情報パターンと第２の生体情報パターンを比較し、この比較に基づいて、患者のストレスを判定するストレス判定部と、
    　と有することを特徴とする情報収集装置。     an acquisition unit that acquires a first biological information pattern in a specific period that goes back a first time and a second biological information pattern that goes back a second time in a specific period;
    a stress determination unit that compares the first biological information pattern and the second biological information pattern and determines the patient's stress based on the comparison;
    An information collection device characterized by having:
  12. 　医師の問診または診断に従って、疾患の原因となる候補を検索し、この原因候補を検証するためのエビデンスデータの送付を患者の携帯端末に要求する要求ステップと、
    　上記携帯端末から上記エビデンスデータを受信すると、該エビデンスデータについての、第１の時間遡る特定期間における第１のパターンと、第２の時間遡る特定期間における第２のパターンを比較する比較ステップと、
    　上記第１のパターンと第２のパターンの比較結果に基づいて、上記原因候補の中から上記患者の疾患の原因を決定する決定ステップと、
    　を有することを特徴とする情報収集方法。     a request step of searching for a candidate causing a disease according to an inquiry or diagnosis by a doctor and requesting the patient's portable terminal to send evidence data for verifying this candidate cause;
    a comparing step of comparing a first pattern of the evidence data in a specific period going back a first time period and a second pattern of the evidence data going back a specific period going back a second time when the evidence data is received from the portable terminal;
    a determining step of determining the cause of the patient's disease from among the candidate causes based on the result of comparing the first pattern and the second pattern;
    An information gathering method characterized by having
  13. 　上記エビデンスデータは、上記患者の生体情報データおよび／または生活習慣情報データであることを特徴とする請求項１２に記載の情報収集方法。 The information gathering method according to claim 12, wherein the evidence data is biometric information data and/or lifestyle information data of the patient.
  14. 　上記原因候補は、ストレス、運動不足、不規則な生活、摂取物、気候の内の少なくとも１を含むことを特徴とする請求項１２に記載の情報収集方法。 The information gathering method according to claim 12, wherein the candidate causes include at least one of stress, lack of exercise, irregular life, food intake, and climate.
  15. 　決定された上記原因に基づいて、上記患者に処方する処方ステップを有し、上記患者への処方を、上記携帯端末に送信することを特徴とする請求項１２に記載の情報収集方法。 13. The information gathering method according to claim 12, comprising a prescription step of prescribing to the patient based on the determined cause, and transmitting the prescription to the patient to the mobile terminal.
  16. 　医師の問診または診断に従って、疾患の原因となる候補を検索する検索部と、
    　上記原因候補を検証するためのエビデンスデータの送信を患者の携帯端末に要求する通信部と、
    　上記携帯端末から上記エビデンスデータを受信すると、該エビデンスデータについての、第１の時間遡る特定期間における第１のパターンと、第２の時間遡る特定期間における第２のパターンを比較する比較部と、
    　を有し、
    　上記第１のパターンと第２のパターンの比較結果に基づいて、上記原因候補の中から上記患者の疾患の原因が決定されると、上記通信部は上記患者に対する処方を上記携帯端末に送信することを特徴とする情報収集装置。     a search unit that searches for a candidate that causes a disease according to an interview or diagnosis by a doctor;
    A communication unit that requests the patient's mobile terminal to transmit evidence data for verifying the cause candidate;
    a comparison unit that compares, when the evidence data is received from the portable terminal, a first pattern in a specific period going back a first time period and a second pattern in a specific period going back a second time period for the evidence data;
    has
    When the cause of the patient's disease is determined from among the cause candidates based on the result of comparison between the first pattern and the second pattern, the communication unit transmits a prescription for the patient to the portable terminal. An information collection device characterized by:
  17. 　携帯端末が依頼信号を受信する受信ステップと、
    　上記依頼信号に応じて、上記携帯端末の記録部に記録された情報の中から依頼に応じた情報を検索する検索ステップと、
    　上記検索された情報を上記携帯端末の表示部に表示すると共に、上記表示された情報の依頼元に送信を許可するか否かのスイッチ表示を行う表示制御ステップと、
    　を有することを特徴とする携帯端末の情報共有方法。     a receiving step in which the mobile terminal receives the request signal;
    a searching step of searching for requested information from information recorded in a recording unit of the portable terminal in response to the request signal;
    a display control step of displaying the retrieved information on the display unit of the mobile terminal and displaying a switch indicating whether or not to permit transmission of the displayed information to a requester of the displayed information;
    An information sharing method for a mobile terminal, comprising:
  18. 　上記スイッチ表示の操作前に、送信を許可する情報と許可しない情報を選択可能とした送信候補情報を表示する送信候補表示ステップをさらに有することを特徴とする請求項１７に記載の携帯端末の情報共有方法。 18. The information of the portable terminal according to claim 17, further comprising a transmission candidate display step of displaying transmission candidate information in which transmission permitted information and transmission non-permitted information can be selected before the operation of the switch display. how to share.
  19. 　上記携帯端末の記録部に記録された情報の中から検索する、依頼に応じた情報としては、少なくとも第１の時間遡る特定期間の第１の生体情報パターンと、第２の時間遡る特定期間の第２の生体情報パターンと、を含む情報であることを特徴とする請求項１７に記載の情報共有方法。 The requested information to be retrieved from the information recorded in the recording unit of the portable terminal includes at least a first biometric information pattern in a specific period going back at least a first time period and a biological information pattern of a specific period going back a second time period. 18. The information sharing method according to claim 17, wherein the information includes a second biometric information pattern.
  20. 　上記携帯端末の記録部に記録された情報の中から検索する、依頼に応じた情報としては、生体情報パターンの時間変化に基づいた情報であることを特徴とする請求項１７に記載の情報共有方法。 18. Information sharing according to claim 17, wherein the requested information to be retrieved from the information recorded in the recording unit of the mobile terminal is information based on a change in biometric information pattern over time. Method.
  21. 　上記携帯端末の上記記録部に記録された生体情報パターンからストレスを判定するストレス判定ステップを有し、
    　上記携帯端末の記録部に記録された情報の中から検索する、依頼に応じた情報としては、ストレス情報であることを特徴とする請求項１７に記載の情報共有方法。     a stress determination step of determining stress from the biological information pattern recorded in the recording unit of the mobile terminal;
    18. The information sharing method according to claim 17, wherein the requested information to be retrieved from the information recorded in the recording unit of the portable terminal is stress information.
PCT/JP2021/027793 2021-07-27 2021-07-27 Information collection method, information collection device, and information sharing method for mobile terminal WO2023007593A1 (en)

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