WO2023007593A1 - Procédé de collecte d'informations, dispositif de collecte d'informations, et procédé de partage d'informations pour terminal mobile - Google Patents

Procédé de collecte d'informations, dispositif de collecte d'informations, et procédé de partage d'informations pour terminal mobile Download PDF

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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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information
patient
pattern
stress
cause
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PCT/JP2021/027793
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English (en)
Japanese (ja)
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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/fr
Publication of WO2023007593A1 publication Critical patent/WO2023007593A1/fr

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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.

Abstract

L'invention concerne un procédé de collecte d'informations et un dispositif de collecte d'informations permettant de sélectionner efficacement des données d'habitude de mode de vie sur la base d'un résultat de diagnostic provenant d'un médecin ou similaire. Le procédé de collecte d'informations comprend une étape d'acquisition (S29) pour acquérir un premier motif d'informations biologiques dans une période spécifique remontant à une première période de temps et un second motif d'informations biologiques dans une période spécifique remontant à une seconde période de temps, et une étape de détermination de stress (S31) dans laquelle le premier motif d'informations biologiques et le second motif d'informations biologiques sont comparés et un stress du patient est déterminé sur la base de la comparaison.
PCT/JP2021/027793 2021-07-27 2021-07-27 Procédé de collecte d'informations, dispositif de collecte d'informations, et procédé de partage d'informations pour terminal mobile WO2023007593A1 (fr)

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JP2010134946A (ja) * 1997-03-14 2010-06-17 First Opinion Corp コンピュータ化された医療アドバイスシステム
JP2006320735A (ja) * 2000-03-14 2006-11-30 Toshiba Corp 身体装着型生活支援装置および方法
JP2007188363A (ja) * 2006-01-16 2007-07-26 Takasaki Univ Of Health & Welfare 個人健康管理システム
JP2010057552A (ja) * 2008-09-01 2010-03-18 Omron Healthcare Co Ltd 生体指標管理装置
JP2012221386A (ja) * 2011-04-13 2012-11-12 Toshiba Corp ホームヘルスケアシステム及び生体情報管理プログラム
JP2015148937A (ja) * 2014-02-06 2015-08-20 株式会社ニコン 生体臭検出装置、生体臭検出システム、および生体臭検出プログラム
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