WO2021140731A1 - Dispositif et procédé de transmission d'informations - Google Patents

Dispositif et procédé de transmission d'informations Download PDF

Info

Publication number
WO2021140731A1
WO2021140731A1 PCT/JP2020/041125 JP2020041125W WO2021140731A1 WO 2021140731 A1 WO2021140731 A1 WO 2021140731A1 JP 2020041125 W JP2020041125 W JP 2020041125W WO 2021140731 A1 WO2021140731 A1 WO 2021140731A1
Authority
WO
WIPO (PCT)
Prior art keywords
information
data
specific
inference
inspection
Prior art date
Application number
PCT/JP2020/041125
Other languages
English (en)
Japanese (ja)
Inventor
野中　修
智子 後町
弘達 藤原
亮 櫻井
Original Assignee
オリンパス株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by オリンパス株式会社 filed Critical オリンパス株式会社
Priority to CN202080085581.7A priority Critical patent/CN114868203A/zh
Publication of WO2021140731A1 publication Critical patent/WO2021140731A1/fr

Links

Images

Classifications

    • 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 transmission device and an information transmission method capable of providing a user with customized information such as advice according to an inspection result that can be obtained in daily life.
  • the Internet has become widespread in recent years, and by using the Internet, it is possible to easily obtain information closely related to the user's life.
  • various customized information valid information
  • services that provide this customized information are increasing. For example, services that introduce health foods are often interesting information that is common to many people, so this type of service is often found.
  • Patent Document 1 discloses a remote inspection method for transmitting inspection data by using a sensor chip and a mobile phone as a reader / writer and using a public communication network. Then, in Patent Document 1, it is proposed to store past inspection data and its evaluation results in a database and use these information.
  • Patent Document 2 discloses a biometric information measuring device that integrates personal authentication data and image data photographed by excrement photographing means and transmits the integrated data by communication means. Further, in Patent Document 3, a study list related to an examination is displayed, a medical image based on the medical image information of the subject in the selected study is displayed on the image display screen, and when a history browser is requested, the study of the subject is requested. A method of displaying medical information that displays a history browser representing a list of medical information is disclosed.
  • Patent Document 4 discloses that information according to the patient's situation and symptoms is presented based on the patient's interview information, past diagnostic information such as test values, hospital information, and the like.
  • Patent Document 5 discloses that a medical person checks a change pattern of a patient's time-series examination data on a predetermined time axis.
  • Patent Document 6, Patent Document 7, and Patent Document 8 disclose that time series data is input to an inference engine to obtain an inference result.
  • Patent Document 9 discloses that it assists in determining the optimum inspection interval.
  • Patent Document 10 discloses that model learning is performed using the result data of biochemical tests of samples such as blood, urine, and stool, and information is provided using this model.
  • JP-A-2009-258886 Japanese Unexamined Patent Publication No. 2014-031655 Japanese Patent No. 5294947 Japanese Unexamined Patent Publication No. 2016-018457 Japanese Unexamined Patent Publication No. 11-089822 JP-A-2018-518207 International Publication No. 2019/022085 Japanese Unexamined Patent Publication No. 2006-511881 Japanese Unexamined Patent Publication No. 2011-253464 Japanese Unexamined Patent Publication No. 2019-21186
  • Patent Document 1-3 describes that biometric information is acquired and this information is remotely transmitted and used. If the subject has no subjective symptoms, it is useful to know that he / she is ill and that he / she may become ill. However, Patent Document 1-3 does not describe anything about generating customized information such as advice according to an individual's profile and notifying the user of this customized information.
  • Patent Document 6-8 discloses that inference is performed using an inference model and information is provided to the subject.
  • the inspection data may change significantly when there is a certain event (trigger).
  • a certain event Trigger
  • the impact event that affected the change in the user's health condition is known, the user can subsequently pay attention to the impact event and maintain his / her health.
  • the present invention has been made in view of such circumstances, and an object of the present invention is to provide an information transmission device and an information transmission method capable of presenting an event related to a change in a user's health condition.
  • the information transmission method acquires inspection data in a time series of a specific period using an inspection device having a specific specification, acquires change pattern information of the inspection data, and obtains the change pattern information of the inspection data. Inference that the effect event information at the time corresponding to the time when the change pattern changes within the specific period is detected for the change pattern information, and the result of annotating the detected effect event information is learned as teacher data.
  • An impact event is inferred by acquiring a model and inputting inspection data obtained in time series in a period similar to the width of the specific period using the inspection equipment of the specific specifications of a specific person into the inference model. And convey the result of the inference.
  • the information transmission method corresponds to the input of a specific health response event, and prior to the above health response event, inspection data is acquired and recorded in time series using an inspection device having specific specifications.
  • an inference model is acquired in which the result of annotating the equipment and / or equipment and / or environment information at the time of the above-mentioned health response event is trained as teacher data with respect to the change pattern information of the test data. Then, the inspection data obtained in time series using the inspection equipment of the specific specifications of the specific person is input to the inference model to infer the information of the equipment and / or the equipment and / or the environment. From the inference result, the health response event customized for the specific person is transmitted to the specific person.
  • the information transmission method includes change pattern information of the above data created by acquiring a plurality of inspection data in a time series over a specific period using an inspection device having a specific specification, and the above-mentioned identification. Create a database that can record the timing of each of the plurality of event information related to the acquisition source of the inspection data within the period of the above, and use the database to determine the trend change of the change pattern and the timing of the event. Based on the relationship, it is possible to extract the events that have influenced the above-mentioned trend change from the above-mentioned event information and provide the information.
  • the information transmission method according to the fourth invention is based on the above-mentioned component in the above-mentioned third invention, in which the above-mentioned event information is decomposed for each component and components are extracted, and the event information affecting the above-mentioned trend change is extracted. Customize and provide information.
  • the information transmission method is the inference model for providing an event influencing the trend change by using the database in the fourth invention.
  • the inference model acquires inspection data and obtains the inspection data.
  • a change pattern is used as an input of the inference unit for learning, and advice to be output is used as annotation information to generate an inference model.
  • the change pattern information of the target person is used as the inference model.
  • the inference result is obtained by inputting, and the above-mentioned transmission information is determined based on the obtained inference result.
  • the information transmission device includes a data acquisition unit that acquires inspection data in a time-series manner for a specific period using an inspection device having a specific specification, and acquires change pattern information of the inspection data, and the above-mentioned change.
  • a data acquisition unit acquires inspection data in a time-series manner for a specific period using an inspection device having a specific specification, and acquires change pattern information of the inspection data, and the above-mentioned change.
  • an inference model is obtained in which the influence event information at the time corresponding to the time when the change pattern changes within the specific period is detected and the result of annotating the detected influence event information is trained as teacher data.
  • the influence event is input by inputting the inspection data obtained in time series in the width of the specific period and the period similar to the width of the specific period using the learning unit to be acquired and the inspection device of the specific specification of the specific person into the inference model. It has an inference unit for inferring and an information transmission unit for transmitting the result of the inference.
  • the information transmission device corresponds to the input of a specific health response event, and data for acquiring and recording inspection data in a time series using an inspection device having specific specifications prior to the health response event.
  • the result of annotating the information on the equipment, / or equipment, and / or environment at the time of the above-mentioned health response event with respect to the change pattern information about the above-mentioned inspection data recorded by the acquisition unit is used as the teacher data.
  • Equipment and / or equipment by inputting the inspection data obtained in time series using the learning unit that acquires the trained inference model and the inspection equipment of the above-mentioned specific specifications of a specific person into the inference model. It also has an inference unit that infers environmental information and / or an information transmission unit that transmits a health response event customized for the specific person from the inference result by the inference unit to the specific person.
  • the information transmission device is created by a data acquisition unit that acquires a plurality of inspection data in a time-series manner over a specific period using an inspection device having a specific specification, and an inspection data that is acquired and created.
  • a data creation unit that creates a database that can record the change pattern information of the inspection data and the timing of each of a plurality of event information related to the acquisition source of the inspection data within the specific period, and the database. Based on the relationship between the trend change of the change pattern and the timing of the event, the information providing unit that can extract the event that affects the trend change from the event information and provide the information. Have.
  • an information transmission device and an information transmission method capable of presenting an event related to a change in a user's health condition.
  • the present invention as an example of grasping the accurate health condition by considering the situation of the subject and providing customized information, the profile information of the subject is stored, and the inspection data regarding the health condition is collected every day.
  • the subject in this embodiment is a person who may become a patient, depending on the re-examination.
  • this subject is also a person who can regain confidence in health, do not have to worry about illness, and enjoy daily life.
  • he is also a person who can become healthy by simple improvement and treatment of life.
  • the information transmission system has an inspection data acquisition unit (for example, refer to the information determination device 2 in FIG. 1) that acquires the inspection data of the subject, profile information of the subject, and information on the possessed equipment for each medical institution.
  • a storage unit for example, see DB 8 in FIG. 1) to be stored, and a transmission information determination unit (for example, a transmission information determination unit) that determines transmission information to the target person according to test data, profile information of the target person, and possessed device information for each medical institution.
  • This information transmission system is composed of, for example, a server, but may be configured by a personal computer capable of exchanging information with the server, a mobile information device such as a smartphone, or the like.
  • the information transmission system is sent to the inspection data acquisition unit (for example, refer to the information determination device 2 in FIG. 1) that acquires the inspection data of the subject, the time-series change pattern of the inspection data, and the medical institution of the subject. It has a transmission information determination unit (see, for example, control unit 1 and inference engine 7 in FIG. 1) that determines transmission information to a subject according to an inference model generated by machine learning using the visit information of the above.
  • the visit information is not limited to medical institutions, but also includes information when a person is examined at a medical institution or when a drug is purchased and taken at a pharmacy or the like.
  • This information transmission system is also composed of, for example, a server, but may also be composed of a personal computer capable of exchanging information with the server, a mobile information device such as a smartphone, and the like.
  • DB database
  • the maintenance of medical equipment delivered to medical institutions is carried out by the medical equipment manufacturer that delivered it, and installation equipment is required for each facility or patient, so a service business for equipment management service provision type is formed. It's getting better.
  • the database used in this embodiment may be the one in this service.
  • This service is configured to centrally manage including customer information and provide configuration management and change management functions for installed equipment based on customer data such as clinical departments, various patient groups, doctors, and nurses. By registering items (configuration items) and sequentially updating the configuration information of installed equipment, it is easier to manage the range of influence when upgrading or replacing parts. Therefore, it is possible to predict the replacement time and maintain the quality of the installed equipment.
  • medical institutions have been described here, as an exception, information on equipment and / or equipment and / or environment at the time of a health response event, including medical treatment at medical institutions, is also described above. It is assumed that it will be centrally managed within the service of.
  • a health response event is an event that has an impact on health. For example, a user goes to a medical facility to see a doctor, or purchases and takes medicine at a pharmacy, which is related to health. It is an act to do. In addition, users went to the gym for training, exercised tennis, etc., and also had binge eating at restaurants, lacked sleep, and worked or played in the cold. Any act that directly or indirectly affects health, such as.
  • an inference engine may be used to determine the information to be transmitted to the target person by inference.
  • the inspection data of the target person is input to the inference engine, and the information transmitted to the target person is acquired.
  • the reliability of the inference result is determined, and if the reliability is low, teacher data is collected and learning for generating an inference model is requested (see, for example, S8, S10, etc. in FIG. 7).
  • Teacher data is created by obtaining data similar to the data input to the inference engine. When a new inference model is generated, this inference model is used to determine the information to be transmitted to the target person.
  • the time-series test data changes (see, for example, FIG. 8 (a)). Impact events including such environmental changes can be inferred from the user's inspection data by generating an inference model using the time-series inspection data of other users and the like. .. That is, if test data for mainly inspecting biological information is acquired in a time series for a specific period using a test device having a specific specification and the changes are arranged in a time series, the test data changes every moment. A change pattern is obtained.
  • the tendency of the change of the health condition or the physical condition of the person can be obtained by observing the change of the numerical value. If this value is constant over time, it is considered that the health condition is kept constant from the value. In addition, if there is a gradual change due to the rhythm of life, the influence of the season or climate, growth, aging, etc., it may be healthy, so it does not matter. That is, the time-dependent change pattern of the inspection data is gradual or regular, but is substantially constant.
  • the measured value will change in some way. If what is written as a pattern here is a continuous minute change in data, the expression that the pattern changes means that the measured value has a tendency (trend) even though it changes slightly, that the trend is changing, and that the trend is changing. When the above-mentioned pattern of meaning is changing, it is confusing if the two expressions are not distinguished. Therefore, if there is a cycle in which the fluctuation is small, but it rises or falls, and there is a cycle, the term that expresses the situation in which it changes from normal may be rephrased as a trend change.
  • the pattern small fluctuations centered on a specific value
  • the trend is also constant.
  • the numerical value may be different from the previous tendency, or it may fluctuate depending on the change slope or fluctuation range, and the tendency (trend) up to that point may be changed. If it does not return (if it changes to a different trend due to a peculiar change), it is probable that the person had some change in health or physical condition. At this time, it is assumed that the trend of the pattern has changed.
  • the tendency of such a change pattern (this is a steady change) is monitored, and the tendency (maximum value, minimum value, average value, fluctuation cycle, etc.) in a specific period such as one day, one week, or one month unit. Record and understand the tendency of fluctuation range). Then, the newly obtained test results may be collected into a pattern, and the individual test values of this pattern may be compared to determine whether the tendency of this pattern has changed the tendency (trend) so far. Of course, if a numerical value with a significantly different trend appears, it is clearly considered to be different from the trend. Further, it is not necessary to make a single comparison, and it may be determined by the tendency of the pattern of a cohesive data group, such as when the tendency change continues.
  • the event when there is a change in the tendency of the change pattern affects the physical condition and health, so it is tentatively called an influence event.
  • the trend of the change pattern changes within a specific period (in a broad sense, it may be expressed that the pattern has changed, so in order to avoid complication, the trend of the pattern has changed during the following examples.
  • Detecting the impact event information at the time corresponding to (sometimes expressed as a pattern change) (you may search for the recorded one or search for external information at that time) is the person. It can be important information for health management not only for other people but also for other people.
  • the result of annotating the detected influence event information can be obtained as teacher data used for inference model learning for event inference for health management.
  • An inference model trained from such teacher data is acquired, and the inspection data obtained in time series in a period similar to the width of a specific period using an inspection device of a specific specification of a specific person is used as an inference model.
  • the reason why I wrote the width of a specific period here is that the change in data obtained over many years and the change in data obtained in one week are not correct comparisons when comparing test data between the same people. So, in the case of numerical values that change in the morning, day and night, when waking up, when exercising, etc., even the characteristics of the change pattern (small fluctuation centered on a specific value) that considers those effects are compared as a pattern that correctly captures To do.
  • the above period may be selected based on the following concept.
  • the above-mentioned similar period at the time of information acquisition follows the recommended specifications of the database and inference model. It is a period corresponding to a specific period to be decided, and waits until future test results are collected.
  • the above-mentioned similar period at the time of information acquisition has already been acquired by the person who has already received the information, or a caregiver or a person concerned.
  • Time-series data of a period similar to the period corresponding to the period (and may be selected with a similar tendency) is selected from the database and used. Also, select an inference model that uses the data of such a period, or create a new inference model by converting the data of a similar period in the database into teacher data and use it.
  • the timing that I want to know now for example, when I wake up or before going out and I have to decide what to do in the future
  • the complementary information as described above may be used.
  • the period is set to be approximately the same.
  • the database assumed here is assumed to include event information that the trend of the change pattern of the test data changes depending on the event, and even if the database is divided for each event that affects health. Good.
  • event information that the trend of the change pattern of the test data changes depending on the event, and even if the database is divided for each event that affects health.
  • the trend change of the test result and its cause can be estimated, and the person who receives the information can pay attention to the maintenance of health and the prevention of improvement and deterioration.
  • This information transmission system includes a control unit 1, an information determination device 2, a terminal 4, a learning unit 5, an inference engine 7, a database (DB) unit 8, and related inspection institutions (including medical institutions) 9. It should be noted that this database may be mediated by a plurality of linked databases and computers.
  • the control unit 1 is arranged in the server, and the information determination device 2, the terminal 4, the learning unit 5, the inference engine 7, the DB unit 8, and the related inspection organization 9 are connected to the server via a network such as the Internet. It is possible to connect.
  • control unit 1, the information determination device 2, the learning unit 5, the inference engine 7, and the DB unit 8 may be a server. It may be arranged inside, and the others may be arranged in another electronic device such as a server or a personal computer. Further, the related inspection agency 9 may have a server function.
  • the control unit 1 is a controller that controls an information transmission system according to the present embodiment, and is a CPU (Central Processor Unit), a memory, and an HDD (Hard Disc) that provide files, data, and the like to a server and the like and other terminals via a network. It is assumed that the IT equipment is composed of Drive) and the like. However, the control unit 1 is not limited to this configuration, and when it is constructed as a small-scale system, it can be configured with something like a personal computer.
  • the control unit 1 has various interface circuits, can cooperate with other devices, and can perform various arithmetic controls by a program.
  • the control unit 1 receives information from each linked device, organizes the information, generates necessary information, and provides this information to the user.
  • the control unit 1 also has a function of outputting a request to each of the linked devices and operating each device.
  • the information determination device 2 the terminal 4 and the like owned by the target person, and the control unit 1 by wireless communication or wired communication. ..
  • a wireless LAN or a mobile phone communication network is assumed, and short-range wireless communication such as Bluetooth (registered trademark) or infrared communication may be used in combination depending on the situation.
  • the description of the communication unit including the communication circuit, the antenna, the connection terminal, etc. is complicated, so it is omitted in FIG. 1, but the communication unit having the communication circuit, etc. is provided in the part of the arrow indicating the communication in the figure. Has been done.
  • the control unit 1 includes a communication control unit 1a, an ID determination unit 1b, an information provision unit 1c, an inference model specification determination unit 1d, an inference request unit 1e, and a search unit 1f.
  • Each of these parts may be realized by software by a CPU, a program, or the like in the control unit 1, may be realized by a hardware circuit, or may be realized by coordinating software and a hardware circuit. You may. Further, in FIG. 1, since each unit in the control unit 1 cooperates with each other to perform each function, the direction of the signal is omitted, but this will be described separately with a flowchart. For example, in a step like S1 in FIG. 4, the ID determination unit 1b collects information from the information determination device 2 and the like for each of the same target persons.
  • the communication control unit 1a has a communication circuit and the like, and includes an information determination device 2, a terminal 4, a learning unit 5, an inference engine 7, a database (DB) unit 8, and a communication unit provided in the related inspection organization 9. Send and receive data, etc.
  • Each device / part such as the information determination device 2 and the terminal 4 also has a communication unit, but the illustration is omitted in FIG. 1 because it is complicated.
  • the ID determination unit 1b collects information from the information determination device 2 and the like for each of the same target persons. An ID is assigned to each individual in order to identify the individual whose information has been acquired by the information determination device 2. In the present embodiment, since the data of each user is handled, the ID determination unit 1b manages which user's information is received and which user is given the guide. The determination of the specific user is that the information determination device 2 has a biometric authentication function, the user communicates with the terminal 4 through the communication unit in the information determination device 2, and the terminal 4 reads a unique code. Do by. In addition, in order to protect personal information, necessary parts are encrypted and management is strict, but since these are general-purpose technologies, detailed description will be omitted.
  • the information providing unit 1c has a function of acquiring user information (may refer to the result acquired by another device) in order to provide correct information to the user.
  • the information providing unit 1c includes inspection data of the user (specified by the ID) acquired from the information determination device 2 and the like, various information acquired from the related inspection organization 9, and a possessed device stored in the DB unit 8.
  • this information is provided to the user.
  • the information providing unit 1c provides the user with information for recommending a facility suitable for receiving an examination or treatment.
  • the information providing unit 1c inputs the inspection data transmitted from the information determination device 2.
  • this data is inspection data (time series information) to which time information is attached, and is stored in the DB unit 8 or the like with a data structure that can be graphed as shown in FIG.
  • the control unit 1 provides information to the user using the information from the information determination device 2, but the server having the related inspection organization 9 collects the information in the same manner. It may be a modified example such that
  • the information providing unit 1c may acquire lifestyle habits such as the user's address, behavioral style at the place of work, eating habits, bedtime, and meal timing on the Internet, and the acquired information is also taken into consideration. Therefore, information such as facilities to be provided to the user may be generated. The acquisition of this information can be complemented by general-purpose or well-known technology.
  • the information providing unit 1c may also customize the information of the facility or the like generated by acquiring the information. Specifically, it is conceivable to provide information on a clinic near the user's residence (recorded as the user's profile information). However, if the clinic does not have the essential inspection equipment, it is not possible to estimate the cause of the disease, take countermeasures, or treat it. Therefore, we will provide information by adding profile information related to the facilities of the clinic, the specialty of the doctors who work, and other facilities. Profile information about this facility is obtained as medical institution information from the related inspection institution 9.
  • the information providing unit 1c uses the information on the possessed equipment, etc. stored in the DB unit 8 in addition to the information collected from the information judgment device 2 and the related inspection organization 9.
  • the information recorded in the DB 8 may be recorded in a different recording unit other than the DB unit 8. In this case, there are a plurality of DB parts in FIG. 1, but they are omitted because they are complicated.
  • the information providing unit 1c collects various kinds of information in providing the information. That is, the information providing unit 1c functions as an acquisition unit for acquiring the test data of the target person, the profile information of the target person, and the possessed device information for each test / medical institution.
  • the information providing unit 1c determines whether or not there is specific information in the user's inspection data transmitted from the information determination device 2 and the related inspection organization 9, and if the specific information is detected, further inspection is performed. (See S1, S3, S13 in FIG. 4, S1, S3, S13a in FIG. 7).
  • the specific information is information related to a disease, for example, a numerical value having a difference from a healthy state or a trend (change) of a change pattern (a minute change centered on a specific value). If the value of the specific information itself deviates significantly from the standard value, or if the trend change of the change pattern is remarkable, there is a high possibility that there is some kind of disease.
  • the specific information may be information that can be suspected of a specific disease.
  • the facility recorded in the DB section 8 may be searched when presenting the recommended facility (see FIG. 4).
  • the DB unit 8 is constructed with a database of necessary tests for each specific information or disease, and a database of medical facilities, testing institutions, etc. having testing equipment / equipment for performing these tests. Just leave it.
  • an inference engine may be used to infer advice to the user (see FIG. 7). In this case, when the user's history data or the like is input, an inference engine in which an inference model for inferring the user's disease, necessary tests, recommended facilities, etc. may be used may be used.
  • the information providing unit 1c functions as a transmission information determination unit that determines the transmission information to be transmitted to the target person according to the test data of the target person, the profile information, and the possessed device information for each test / medical institution (S7 in FIG. 4). , S9, S11, S13, etc.).
  • the transmission information determination unit inputs the change pattern (trend in which minute fluctuations centered on a specific value change while shifting the value) information of the target person extracted in a specific time width of the target person into the inference model. As a result, an inference result is obtained, and the transmission information is determined based on the obtained inference result (see, for example, S9 in FIG. 4).
  • the transmission information determination unit determines information on the recommended medical institution for undergoing the necessary examination, or information on the timing when the subject visits the examination / medical institution as transmission information (Fig.). 4 S7, S9, S11, S13, timing Tc and the like in FIG. 3A).
  • the transmission information determination unit described above inputs the change pattern of the inspection data acquired by the inspection data acquisition unit (for example, the information determination device 2) into the inference unit (for example, the inference engine 7), and uses the inference result of the inference unit as the inference result.
  • the transmitted information is determined based on this (see S35 in FIG. 5).
  • the transmission information determination unit is the transmission information that determines the transmission information at a time point after the extraction of the time-series pattern to be transmitted to the target person.
  • the transmission information determination unit extracts the change pattern of the inspection data in a specific time width, inputs the extracted change pattern to the inference unit, and acquires the inference result from the inference unit (for example, FIGS. 3A and 3B). ), See S35 in FIG. 5).
  • the transmission information determination unit described above makes an inference by the inference unit when the change pattern of the inspection data acquired by the inspection data acquisition unit is within a specific range (see, for example, S27 and S35 in FIG. 5). ..
  • the transmission information determination unit does not perform inference by the inference unit when the change pattern of the inspection data acquired by the inspection data acquisition unit is outside the specific range (see, for example, S27 and S29 in FIG. 5).
  • the transmission information determination unit makes an inference by inputting a change pattern of the inspection data of the target person into the inference unit, and determines the transmission information to be transmitted to the target person based on the inference result (for example, S35 in FIG. 5). reference).
  • the information transmission determination unit inputs the time-series change pattern of the inspection data of the subject into the inference unit, the inference unit makes an inference, and based on this inference result, the time-series pattern to be transmitted to the subject is later than the time point. Determine the information to be transmitted in.
  • the inspection data acquisition unit acquires inspection data that is a time-series pattern of the target person for a specific period.
  • This acquired time-series pattern is composed of individual inspection data acquired by measurement at a plurality of different timings, not simply data obtained by one-time measurement, and even changes in the inspection data pattern are used as information. ..
  • a time-series pattern consisting of multiple inspection data it is less susceptible to errors caused by changes in the measurement environment and conditions.
  • it infers the health condition from the end of the specific period to the future period (when the specific period is extended), and makes it possible to predict the future.
  • teacher data can be created by adding the timing information of the subject's examination / visit to the medical institution as annotation information to the acquired time-series pattern. If there is an inference part that has an inference model generated by learning using this teacher data, what is the timing (when the specific period is extended) beyond the specific period (period for acquiring the time series change pattern)? Can be inferred if When generating the inference model used here, the specifications of specific input / output information are specified and learning is performed.
  • the time-series change pattern of the test data of the subject is input to the inference unit, the inference unit makes an inference, and based on this inference result, the transmitted information at the timing from the specific period to the future is transmitted.
  • a transmission information determination unit for determining is provided. Therefore, it is possible to provide a system, an apparatus, a method, a program, or the like capable of transmitting the prediction information at the timing after the inspection acquisition of the time series pattern.
  • the reliability of the inspection data of the subject will be reduced if there is a difference in mechanical performance for each inspection device. Therefore, a large amount of change pattern information of inspection data may be acquired by using the same type of inspection equipment (inspection equipment having specific specifications) and treated as big data.
  • the specific period does not have to be a fixed period, and may be a different time width (specific period) depending on the situation.
  • the "time width” here is not the time between measurement timings (inspection interval / measurement interval), but the time interval from the first measurement to the last measurement when acquiring a series of inspection data. Say.
  • the "time width” may be rewritten as a specific time width including a lot of time series data in this time width and change pattern information of the inspection data.
  • the information providing unit 1c which functions as a transmission information determining unit in the present embodiment, inputs a change putter of inspection data into the inference engine 7 in which the inference model generated by the learning unit 5 is set, and infers the result regarding advice. Is obtained and provided to the user corresponding to the input inspection data.
  • This service may use personal information, and may require a contract for personal information in order to receive advice. In that sense, the user's profile information may be important.
  • advice may be delivered to a person who takes care of the user, a caregiver, or the like. In this case as well, valid information such as advice arrives according to the information managed by the user's profile information.
  • the information providing unit 1c functions as a data acquisition unit that acquires inspection data in a time series of a specific period using an inspection device having a specific specification and acquires change pattern information of the inspection data (for example, FIG. 8 (b), see S61 and S63).
  • the information providing unit 1c responds to the input of a specific health response event, and functions as a data acquisition unit that acquires and records inspection data in chronological order using an inspection device having specific specifications prior to the health response event ( For example, see S61 and S63 in FIGS. 8 (a) and 8 (b)).
  • the information providing unit 1c functions as an information transmitting unit that transmits the result of inference.
  • the information providing unit 1c functions as an information transmission unit that transmits a health response event customized for a specific person from the inference result by the inference unit to the specific person.
  • the information providing unit 1c functions as a data acquisition unit that acquires a plurality of inspection data in a time series over a specific period using an inspection device having a specific specification.
  • the information providing unit 1c uses a database to extract events that affect the trend change from the event information based on the relationship between the trend change of the change pattern and the timing of the event, and makes it possible to provide the information. Functions as.
  • the inference model specification determination unit 1d determines the specifications of the inference model to be generated when the inference request unit 1e requests the learning unit 5 to generate the inference model.
  • the control unit 1 acquires the biometric information of the user from the information determination device 2 and the like, and accumulates the biometric information.
  • the control unit 1 requests the learning unit 5 to generate various inference models using the accumulated biological information as teacher data.
  • the inference model specification determination unit 1d determines what kind of specification the inference model is requested in generating the inference model. For example, as shown in FIG. 3A, which will be described later, when time-series biometric information is accumulated, what kind of inspection data (value) does the inference model specification determination unit 1d have, and what is the user?
  • the inference model specification determination unit 1d determines specifications for generating an inference model that infers a facility recommended for receiving further necessary tests and treatments based on time-series biological information.
  • the inference request unit 1e requests the learning unit 5 to generate an inference model of the specifications determined by the inference model specification determination unit 1d. That is, the inference requesting unit 1e requests the learning unit 5 to generate an inference model when a predetermined number of biological information acquired by the information determination device 2 is accumulated, and receives the generated inference model. This received inference model is transmitted to the inference engine 7.
  • the control unit 1 may prepare a plurality of inference models and appropriately select the inference model according to the information to be provided to the user.
  • the search unit 1f is an inspection institution or medical institution having equipment necessary for the inspection or treatment when it is found that further examination or treatment is necessary based on the biometric information of the user acquired by the information determination device 2. Is searched in the database stored in the DB unit 8.
  • the search unit 1f functions as a search unit for searching for an event that affects the trend change in the event information based on the relationship between the trend change of the change pattern and the event timing using the database.
  • the information determination device 2 is a device for acquiring test data such as health-related information of the target person, for example, vital information and sample information.
  • health-related information there are various kinds of information, for example, vital information such as body temperature, blood pressure, and heartbeat of the subject.
  • sample information such as excrement such as urine and stool of the subject, sputum and blood.
  • the information determination device 2 acquires the color, shape, amount, and date / time information.
  • the information determination device 2 may acquire information according to an instruction from the control unit 1, may acquire information according to a user's operation, or may automatically acquire information.
  • the information determination device 2 is used for daily life such as daily life, work / school activities, meals, sports activities, etc. in the information "Personal Health Records (PHR)" which is medical / health information. Personal Life Records (PLR), which includes various activity data of the above, may be collected and utilized.
  • PHR Personal Health Records
  • the acquired information is controlled through a communication unit (not shown) in the information judgment device 2. It is transmitted to part 1.
  • the information determination device 2 functions as an inspection data acquisition unit that acquires inspection data of the target person.
  • the information determination device 2 functions as an inspection data acquisition unit that acquires inspection data that is a time-series pattern of the target person for a specific period.
  • the inspection data of the target person acquired by the inspection data acquisition unit is obtained by acquiring inspection data in chronological order using an inspection device having a specific specification and extracting change pattern information of the inspection data in a specific time width. is there. That is, as the information judgment device 2, an inspection device of a specific specification (inspection device of the same type) is used, and the information judgment device 2 measures the inspection items of the same subject at different timings in chronological order. Get the data to.
  • a change pattern can be obtained by drawing the measured values on a graph according to the inspection timing.
  • Inspection data can be obtained by extracting this change pattern in a specific time width.
  • the inspection data is based on a color sensor for defecation, a shape sensor, a hardness sensor, an olfactory sensor (including reaction judgment of nematodes and animals), a gas component sensor, a color change detection sensor when a specific reagent is added, and a magnified observation image. It is the data obtained according to one of the output results of any of the shape determinations.
  • a wearable terminal When a wearable terminal is used as the information determination device 2, vital information such as body temperature, heartbeat, blood pressure, brain wave, line of sight, respiration, and exhalation can be obtained by closely contacting the skin or the vicinity of the body depending on the wearing part of the wearable terminal. Is possible.
  • a sphygmomanometer As a scale, a sphygmomanometer, and a measuring instrument for measuring arterial stiffness, which means the hardness of the arterial wall, dedicated precision equipment is installed in health facilities, public baths, pharmacies, shopping malls, etc. The measurer may also be assigned. In such facilities, users often use the measuring device comfortably in their spare time and manage their physical condition based on the measurement results at that time. These measuring devices may be used as the information determination device 2.
  • the information judgment device 2 may request the user to fill out a questionnaire before and after using a dedicated terminal or the like. In such cases, the user's profile information and other information can be identified based on the description in this questionnaire. Such information collection is not limited to the information determination device 2, and may be performed by the control unit 1. This information can be used when determining whether or not the specific information has been acquired in step S3 of FIG. 4, which will be described later. If information such as when the doctor went to the doctor can be heard, it can be used as the time Tc information in FIGS. 3 (a) and 3 (b) described later.
  • the information determination device 2 obtains information on a specific user
  • the information on the facility recommended by the information providing unit 1c of the control unit 1 is presented to the information terminal 4 of the specific user.
  • the explanation is given on the assumption that this presentation assists the user's behavior, but various variations can be considered.
  • the information determination device 2 may be a thermometer or a sphygmomanometer that is already suffering from a specific disease and is used under the guidance of a doctor.
  • the mobile terminal (smartphone) provides the information as it is. It can be the judgment device 2.
  • the extent to which the information determination performed by the information determination device 2 or the like is determined may be changed in relation to the control unit 1.
  • the information determination device 2 or the like may transmit the sensing result to the control unit 1 without determining only the result.
  • the attached information is associated with which person and which sensing result, but it may be associated with the information of another terminal by adding it.
  • the related inspection institution 9 is a facility where the user is inspected, and there are, for example, an inspection facility and a medical facility.
  • the related inspection organization 9 may be a mobile type, for example, a type in which general medical equipment or inspection equipment is mounted on an automobile, train, ship, helicopter, drone, or the like and the patient goes to the patient.
  • the control unit 1 can acquire which medical institution the patient went to, what kind of test result was obtained, and the like from the server that operates the system of the related test institution 9.
  • the server of the related inspection organization 9 may be the same as that of the control unit 1, or some functions may be shared.
  • the related testing institution 9 in the above description, a medical-related test using specific biological biological information or a sample sample from a living body is described.
  • questions and answers such as interviews and subjective symptoms are also tests
  • one of the inspection institutions is one that produces some results by inputting in natural language based on the questions and answers. Be done.
  • the Internet service that provides specific health advice by questionnaire description or item selection is also included in the broad interpretation of the related inspection organization 9.
  • the related inspection organization 9 inputs information corresponding to the category organized and linked for each specific area of the displayed screen, database search and logic-based branching are performed for each item content.
  • inference processing it is possible to provide a service using a technique of displaying health-related advice in characters or by voicing it to the user by voice.
  • the terminal 4 is a mobile information terminal, and is a device for receiving information that can be confirmed by the target person and related persons.
  • the terminal 4 may be, for example, a smartphone or a tablet PC.
  • the built-in camera or microphone can be used as an information acquisition unit.
  • a wearable terminal or other home appliances that can be linked may be used as the terminal 4, and information may be acquired by the wearable terminal or the like. Therefore, the information determination device 2 and the terminal 4 may be the same device, or may be dedicated devices, respectively. Further, depending on the situation, the information determination device 2 or the terminal 4 may have the function of the control unit 1, and may be configured to share detection, control, and information provision.
  • the database (DB) section 8 is a database of information on devices owned by medical facilities and the like. Owned equipment includes measuring equipment used for diagnosis, equipment / equipment for performing treatment, and the like.
  • the control unit 1 refers to the information recorded in the database unit 8 to provide the information regarding the recommended facility.
  • the DB unit 8 has an electrically rewritable non-volatile memory.
  • the DB unit 8 has a recording unit 8a that records a list of owned devices for each facility, and a recording unit 8b that records an ID and a user's visit for each facility.
  • the recording unit 8a records a list of devices owned by facilities such as hospitals, clinics, and inspection institutions.
  • the information providing unit 1c can present the information of the facility having the most suitable equipment for the inspection to the user.
  • the information may be linked with the information of the related inspection institution 9.
  • the recording unit 8b records visit information indicating which person (identified by ID) came at what time for each facility.
  • the recording unit 8b may be omitted.
  • the DB unit 8 is constructed as a part of an information transmission system in which medical facilities cooperate, and the DB unit 8 may also be able to access the related inspection institution 9 through the control unit 1. In this case, when the DB unit 8 receives a search command from the control unit 1, the DB unit 8 searches the data in the related inspection organization 9 in addition to the data recorded in the DB unit 8 to search. Output the result.
  • the DB unit 8 functions as a storage unit that stores the profile information of the target person and the possessed device information for each examination / medical institution.
  • the storage unit is not limited to the DB unit 8, and all or part of its functions may be arranged in the control unit 1 or the like. Details of the data recorded in the DB unit 8 will be described later with reference to FIG.
  • the DB unit 8 includes the change pattern information with specific improvement timing information such as taking medication and the start of life improvement as time information similar to the time information of the change pattern information, so that the part of the change pattern before the improvement information is included. It functions as a data creation unit that creates a database with comparable information.
  • the DB unit 8 creates a database that can record the change pattern information of the inspection data created by acquiring the inspection data and the timing of each of the plurality of event information regarding the acquisition source of the inspection data within a specific period in association with each other. It functions as a data creation unit to be created.
  • the learning unit 5 has an input / output modeling unit 5a and generates an inference model by machine learning or the like.
  • This inference model is generated by learning the relationship between acquired information such as acquired biometric information and biopsy information and the disease, and specifically by learning the relationship between the acquired information and the clinical department / department.
  • the input / output modeling unit 5a Similar to the inference engine 7, the input / output modeling unit 5a has an input layer, a plurality of intermediate layers, and an output layer, obtains the strength of the connection of neurons in the intermediate layer by learning, and generates an inference model.
  • the learning unit 5 detects the influence event information at the time corresponding to the time when the change pattern changes within a specific period with respect to the change pattern information, and learns the result of annotating the detected influence event information as teacher data. It functions as a learning unit for acquiring the inference model (see, for example, S65 to S71 in FIG. 8). In addition, the learning unit 5 annotates the change pattern information of the recorded test data with information on the equipment, / or equipment, and / or environment at the time of the health response event as teacher data. It functions as a learning unit that acquires an inference model trained as (see, for example, S65 to S71 in FIG. 8).
  • the change pattern of the test data acquired from the subject using the same type of test device is extracted in a specific time width, and the extracted change is extracted.
  • the pattern is input to the inference engine 7, and the teacher data is generated using the health advice to be output at a later timing as the annotation information from the timing of the examination by the subject.
  • an inference model is generated by performing learning using this teacher data.
  • the time width that goes back from the time when the test result is obtained has been described, but if the data improves after the test result due to treatment or the like, the treatment may not be successful. You may learn and output prognostic advice.
  • the learning unit 5 can generate an inference model capable of providing lifestyle-related improvement and future prediction advice on the effects of treatment and medication by learning using the examination data sequence after examination, hospital visit, and medication. You can also do it.
  • the time-series data is used starting from the time of examination, outpatient visit, and medication. Use the previous time-series data when giving advice on tests, hospital visits, medications, etc.
  • Deep learning is a multi-layered structure of the process of "machine learning” using a neural network.
  • a typical example is a "forward propagation neural network” that sends information from front to back to make a judgment.
  • the simplest forward-propagating neural network is an input layer consisting of N1 neurons, an intermediate layer consisting of N2 neurons given by parameters, and N3 corresponding to the number of classes to be discriminated. It suffices if there are three layers of output layers composed of the above neurons.
  • Each neuron in the input layer and the intermediate layer, and each neuron in the intermediate layer and the output layer is connected by a connection weight, and a logic gate can be easily formed in the intermediate layer and the output layer by applying a bias value.
  • the neural network may have three layers as long as it makes a simple discrimination, but by increasing the number of intermediate layers, it is possible to learn how to combine a plurality of features in the process of machine learning. In recent years, those having 9 to 152 layers have become practical from the viewpoints of learning time, determination accuracy, and energy consumption.
  • a "convolutional neural network” that compresses the feature amount of the image, performs a process called “convolution”, operates with the minimum processing, and is strong in pattern recognition may be used.
  • a “recurrent neural network” (fully connected recurrent neural network) that can handle more complicated information and whose meaning changes depending on the order or order may be used.
  • a conventional general-purpose arithmetic processing circuit such as a CPU or FPGA (Field Programmable Gate Array) may be used.
  • a processor called GPU (Graphic Processing Unit) or Tensor Processing Unit (TPU) specialized in matrix calculation is used.
  • GPU Graphic Processing Unit
  • TPU Tensor Processing Unit
  • NPU neural network processing unit
  • AI artificial intelligence
  • machine learning examples include, for example, support vector machines and support vector regression.
  • the learning here is to calculate the weight of the discriminator, the filter coefficient, and the offset, and there is also a method using logistic regression processing.
  • humans need to teach the machine how to make a judgment.
  • a method of deriving the judgment of the image by machine learning is adopted, but in addition, a rule-based method of applying the rules acquired by humans by empirical rules / heuristics may be used.
  • the inference engine 7 has an input / output layer and a neural network similar to the input / output modeling unit 5a of the learning unit 5.
  • the inference engine 7 makes inferences using the inference model generated by the learning unit 5.
  • the inference engine 7 is measured by the information determination device 2, inputs time-series biometric information, and, for example, obtains an appropriate inspection institution / medical institution for inspecting, treating, or the like of the user's health condition by inference. ..
  • time-series biometric information it may be inferred when a medical institution will receive a medical examination.
  • the inference engine 7 functions as an inference unit having an inference model learned according to the timing information of the subject's examination / visit to the medical institution.
  • the inference engine 7 functions as an inference unit having an inference model learned according to the change pattern of the person who provided the examination data similar to the examination data of the subject and the examination / visit to a medical institution / examination / medication information.
  • the inference unit acquires inspection data of a person other than the target person using an inspection device of a specific specification, extracts the change pattern of the inspection data in a specific time width, and uses it as an input of the inference unit for learning. Inference using the inference model generated by the learning inference unit using the health advice for the subject to be output from the end of the time width measured by the examiner to a later time as annotation information. I do.
  • the inference engine 7 inputs the inspection data obtained in time series in a period similar to the width of the specific period using the inspection device of the specific specification of the specific person into the inference model to infer the influence event. It functions as an inference unit (for example, inferring when transmitting the inspection results of FIGS. 4, 7, etc. using the inference model generated in FIG. 8 (b)).
  • the inference engine 7 inputs inspection data obtained in time series using an inspection device of a specific specification of a specific person into an inference model to infer information on equipment and / or equipment and / or environment. It functions as an inference unit (for example, using the inference model generated in FIG. 8 (b), inference is made when transmitting the inspection results of FIGS. 4 and 7).
  • the inference model learned according to the subject's visit / examination / medication information is an inference model in which the input / output relationship is set by the teacher data annotated with the input as time-series examination data and the output as related disease information.
  • the related disease information is the date and time of the visit, the diagnosis result, the prescription drug information, and the like.
  • control unit 1 may provide information on the recommended facility by using the inference engine 7 in addition to the search unit 1f searching the DB unit 8.
  • the inference engine 7 infers information about the recommended facility using the inference model generated by the learning unit 5.
  • This inference model is generated by learning the relationship between acquired information such as acquired biometric information and biopsy information and the disease, and specifically by learning the relationship between the acquired information and the clinical department / department. In this way, the control unit 1 may output the guide information to be presented by the inference by the inference engine 7.
  • control unit 1 guides medical facilities, etc. with a single judgment based on the acquired information obtained at one time by search or inference, it unnecessarily brings medical information into life and lives soundly and with peace of mind. May interfere with. Therefore, the accuracy may be improved by using the history (time-series information) of the acquired information a plurality of times.
  • FIG. 3A shows a graph using personal health-related historical data (time-series data) recorded in the recording unit 8.
  • the recording unit 8 records, for example, the specific data A among the data acquired by the specific device A or the data of the device having various inspection functions, and when and which facility the individual visited. doing.
  • the control unit 1 manages the recording of the recording unit 8.
  • the horizontal axis of the graph shown in FIG. 3 (a) is time, and the vertical axis is health-related data.
  • the data posted on this graph can be treated as if the information were arranged two-dimensionally. Therefore, inference can be made by a method similar to image search, in which a specific object is found from this image. That is, the graph may be the input and the output may be the medical institution information. Details of the graph showing the historical data shown in FIG. 3 will be described later.
  • the recording unit 8 records what kind of facility the person whose health-related information has changed is going to or is going to, and when the control unit 1 centrally manages this record, these data are collected. It is possible to have the learning unit 5 create an inference model by using it as teacher data.
  • the inference engine 7 has an inference model obtained by inference model specification determination unit 1d of the control unit 1 designating an inference model specification and learning according to this specification.
  • the inference engine 7 may have a plurality of inference models because the device data may be different when a new device appears.
  • new learning is required each time a new device appears, it is assumed that the inference model is often improved or newly created through the learning unit 5 by the designation of the control unit 1.
  • the information determination device 2 is dedicated and specialized only in inference of a specific disease, it may be a single dedicated inference model.
  • the inference engine 7 is a circuit block centered on an AI chip such as a CPU, GPU, and DSP, like the input / output unit 5a of the learning unit 5, and also includes a memory and the like to form a neural network.
  • the inference engine 7 and the learning unit 5 are connected to a network or the like operated in cooperation with a hospital or the like, and the control unit 1 may be used in cooperation with these. I'm assuming. In this case, there is a possibility that learning and inference information can be exchanged via the related inspection organization 9.
  • the inference engine 7 When the inference request unit 1e of the control unit 1 determines that the information of a specific user has been sufficiently acquired from the information determination device 2 or the like, the inference engine 7 is requested to make an inference.
  • the inference engine 7 inputs an information group showing the time-series transition of similar data, infers based on this information group, and outputs medical institution information (visit information, examination information, etc.) suitable for a specific user. It is possible to do. If a group of data similar to a person with a chronic medical condition is input to the inference engine 7, it is better to display a guide to a facility where the same treatment can be performed.
  • the inference engine 7 can estimate the medical institution that has the equipment to inspect a specific user.
  • the table of FIG. 2 shows the temporal changes and transitions of the data output by the information determination device 2 (test data, biological data, vital data, sample data, etc. are generally expressed as "time series data"). (Explanation as patient ID) indicates what kind of medical institution, etc. was associated with. From this table, it is possible to know when the patient came to any hospital or clinical department based on the patient's behavior history and the history of the in-hospital system.
  • the hospital data recorded in the DB department 8 includes what kind of clinical departments there are, what kind of illnesses have been treated, and what kind of doctors, nurses and other staff are working. Information such as the timetable and other consultation conditions, including what kind of examination can be taken, how long the waiting time is, and whether it is a reservation system or a referral system, is organized and recorded.
  • the consultation conditions may include information such as what kind of questionnaire must be filled out.
  • the consultation conditions may include not only information related to consultation, hospital visit, and surgery, but also information on related rehabilitation facilities, prognosis visit, treatment information, and the like.
  • recording data in a tabular format is possible not only in hospitals but also in pharmacies and public facilities when undergoing examinations.
  • pharmacies there may be information such as what kind of medicines are prepared and provided.
  • the medical institution information is organized including the above-mentioned information and the characteristics of the attached pharmacy, it will be easy to visit in the neighborhood regardless of the medical institution specializing in the treatment of a specific medical condition. You can substitute it by going to your own medical institution. It is possible to select and visit an appropriate medical institution according to the characteristics of the disease, beyond the characteristics of the clinical departments that each hospital / clinic specializes in or has. Such information needs to be reviewed at an appropriate cycle because there are changes in medical staff who work, retirement, and replacement of equipment.
  • medical institution information is not limited to consultations and outpatient visits, but may include information on non-illness and prognosis, and health-related facilities visited by patients (public institutions / facilities that promote or promote recovery of health, etc.) Or, there may be information on stores that provide food and drink that affects the physical condition by ingestion), and what kind of equipment is available is also displayed in a list.
  • Large hospitals have many facilities, and small clinics have limited facilities. The number of cases related to the disease tends to be higher in large hospitals.
  • a specialized clinic specializing in a specific disease can supplement the environment surrounding a small clinic, and can have the same function as a large hospital by cooperating with the clinic in a smart city or the like. Therefore, here, it is possible to present it to the patient as effective information.
  • Such feature information is also organized in chronological order, and in the table shown in FIG. 2, the data output by the information determination device 2 (test data, biological data, vital data, sample data, etc. are generally expressed as "time series data"). You may treat it by incorporating it into the information in the same way as what kind of temporal change or transition the data has changed.
  • It is a data group that summarizes what kind of biological data changes and what kind of lifestyle (dietary tendency, etc.) patients (explained as patient ID) are related to what kind of medical institution.
  • patient ID a data group that summarizes what kind of biological data changes and what kind of lifestyle (dietary tendency, etc.) patients (explained as patient ID) are related to what kind of medical institution.
  • patient ID a data group that summarizes what kind of biological data changes and what kind of lifestyle (dietary tendency, etc.) patients (explained as patient ID) are related to what kind of medical institution.
  • patient ID a data group that summarizes what kind of biological data changes and what kind of lifestyle (dietary tendency, etc.
  • This information transmission device, etc. does not provide typographical advice such as going to a dermatologist or a digestive system clinic, but has similar characteristics within the range that the person can easily access.
  • the characteristic information of medical institutions, health-related facilities, and stores in the vicinity is updated and renewed at a specific timing, and the situation at the time of the change in biometric information is recorded and can be searched.
  • the items shown as "others" in FIG. 2 may include not only the medical institution alone but also the surrounding situation and their past information. That is, test data is acquired in chronological order using a test device having specific specifications, change pattern information of the test data is acquired, and characteristic information (characteristic information) in a medical institution or the like that has an improvement effect on the change pattern information (The inferred result is transmitted by inputting the inspection data obtained in time series using the inspection equipment of the specific specifications of a specific person to the inference model in which the result of annotating the impact event information) is trained as teacher data. It becomes possible to provide an information transmission method characterized by doing so.
  • inspection data is acquired in chronological order for a specific period using an inspection device having a specific specification.
  • the change pattern information of the inspection data is acquired from the inspection data acquired in time series.
  • the influence event information at the time corresponding to the time when the change pattern changes within a specific period is detected.
  • the impact event information is information on the event that contributed to the improvement when it is recognized that the improvement effect was achieved by the medical institution or the like.
  • an inference model is acquired in which the result of annotating the detected impact event information is trained as teacher data.
  • the inference model When the inference model is generated, the inference model is affected by inputting the inspection data obtained in time series during a period similar to the width of the specific period using the inspection equipment of the specific specification of a specific person. Infer the event. When the inference result is obtained, the inference result is transmitted to the user or the like.
  • the time-series pattern here and the database that records this time-series pattern can be said to have the following characteristics.
  • the database can include an example in which the change pattern information of the inspection data is acquired after the specific improvement timing and the feature that the change pattern is surely improved is also included.
  • the timing of improvement if it is possible to determine the similarity of the change pattern of the patient in the future and it contains some information that the change pattern of the biological information of the patient candidate may be improved, many people will be asked. It can be used and become a convenient database.
  • possession equipment includes simple items such as thermometers, stethoscopes, sphygmomanometers, weight scales, and body fat scales, as well as X-ray examination devices.
  • endoscopic ultrasonography equipment stationary equipment such as CT and MRI, and examination equipment and reagents for various infectious diseases.
  • stationary equipment such as CT and MRI
  • examination equipment and reagents for various infectious diseases.
  • these devices can be identified and managed including them.
  • some endoscopes can and cannot observe special light. As a result, the characteristics, types, limits, etc. of the inspections that the facility can perform are determined.
  • the characteristics of the facility including information such as inventory of owned equipment, supplies, consumables, etc. in the column of owned equipment, you can issue an effective guide based on this information. Since the gender of doctors and examination technicians and the existence of private rooms are also useful information for patients, they may be displayed and guided in a database. Whether the toilets in hospitals and other facilities can obtain vital information, sample information, etc., whether they are properly calibrated and highly reliable toilets, or whether they are facilities that support urinalysis and stool tests, and whether they are vitals. It is preferable to acquire information such as whether or not there is a rental device for monitoring the patient's health condition, which can acquire information, as facility information. Not only patients who come to the clinic, but also users who visit the facility to obtain information on the calibrated toilet at the facility, the error between the toilet at home, and the error between the toilet at home and the stool test can be handled.
  • the following inference model is generated to perform inference.
  • the result of annotating the equipment and / or equipment and / or environment information at the time of the health response event is used as teacher data, and the inference model is acquired by training using this teacher data.
  • the inference model is acquired, as described above, the inspection data obtained in time series using the inspection equipment of the specific specifications of the specific person is input to the inference model, and the equipment and / or equipment and / or By inferring environmental information, the above important factors can be obtained. If you convey a health response event customized for a specific person from this inference result (important factor) to a specific person, you can go to a clinic in the neighborhood without having to go to a distant hospital, or improve your life. It may be done.
  • the system it is possible to make inferences including the elements of health response events.
  • information is not given as to whether or not the person's health condition has really improved, but the actions taken by many people can be inferred.
  • a specific health response event (which may be manual or automatic) may be input only when the health condition is improved. Improvement can be judged by changes in inspection data, etc., or may be judged based on a questionnaire. In other words, the system may be such that the hospital sends a questionnaire mail to the patient after the medical treatment, and the health response event is input only when the answer that the symptom of concern has improved is returned.
  • the event information is subdivided based on the environment and situation of the event that affected the health condition and other component information, it can be provided as customized information replaced by the position of the person who provides the information.
  • test data change pattern and the teacher data of a large number of cases obtained by annotating the influence event may be collected and learned to create an inference model.
  • the input may be the change pattern and the output may be the influence event element.
  • the number of cases Nc1 to Nc6 and the like are recorded in the database of the DB unit 8. If there is a number of cases treated by the clinic for the related disease lp, it can be used as a reference when the user selects a medical facility, and can also be used when displaying a recommended medical facility by inference or the like.
  • information such as prognosis information, hospitalization period information, and adjacent pharmacy information (for example, assortment information may be included) may be recorded in other columns of the database of the DB unit 8. ..
  • the search unit 1f of the control unit 1 has a function of accessing the database as shown in FIG. 2 and extracting necessary information.
  • the patient ID is an identification code given to each patient (user) in the ID determination unit 1b to identify the patient.
  • the time series data is individual data measured by the information determination device 2.
  • the data Dy1 (t11) is the data of the type y1 measured at the date and time t11.
  • the data type y is, for example, vital information such as body temperature and blood pressure, and sample information such as stool.
  • the date and time of visit Th indicates information on the date and time when the patient visited a medical institution.
  • the related disease lp indicates information on the disease name assumed from the time series information of the patient ID.
  • Clinics H1 to H4 indicate medical facilities, and indicate medical facilities where a person specified by a patient ID has visited.
  • the clinical departments Dp1 to Dp3 indicate the names of clinical departments in the medical facility where the person specified by the patient ID visited.
  • the possession equipment Mod is the possession equipment associated with the person identified by the patient ID.
  • the number of cases Nc1 to Nc6 is the number of cases handled in each clinic / clinical department. When displaying the recommended equipment to the user, the number of cases may be taken into consideration, or the recommended equipment may be displayed for reference information when the user selects a clinic or the like.
  • FIG. 2 shows a list in which the recording unit 8a of the list of owned devices by facility and the recording unit 8b of the ID and visit information list by facility are mixed.
  • the recorded contents of the recording unit 8a will be obtained.
  • the recorded contents of the recording unit 8b can be obtained.
  • time-series data is recorded for each patient ID, historical data for each individual patient can be created regardless of the medical facility that received the treatment. This historical data can also be used when making inferences in S9 of FIG. 4 and S6 of FIG. 7, which will be described later.
  • FIG. 3 is a graph created using inspection data.
  • FIG. 2 shows an example of time-series inspection data recorded in the DB unit 8.
  • examination data organized in chronological order is recorded for each patient ID
  • FIG. 3 is a graph showing the examination data shown in FIG.
  • the horizontal axis shows the time T
  • the vertical axis shows the time series data in FIG. 2 plotted.
  • this is test data, biological data, vital data, and sample data, and the test output result of the device that inspects any of these is represented by a numerical value D. For example, it is a value indicating the degree of red color of stool.
  • FIG. 3 it is assumed that the date and time of visit and the like are automatically updated systematically.
  • the example shown in FIG. 3A is a case in which the time-series data changes in the direction of deterioration of health and the user eventually goes to the hospital. Therefore, the pattern of each patient described in FIG. 2 Closest to.
  • the information as explained in FIG. 2 is also used to provide the result of inferring how long and what kind of clinical department the patient will go to. It is possible to do.
  • a table as shown in Fig. 2 is created. It can. However, this does not apply to the table in FIG. 2 because some people have only vital data even if they do not go to the hospital at all.
  • Fig. 3 (a) is a case in which it is presumed that the patient will go to the hospital in the future.
  • the graph shown in FIG. 3A shows changes in time series of examination data (equipment data) of users who are not currently visiting the hospital. From this time-series test data, it is possible to obtain information on whether or not to visit a medical institution when a specific test result (specific information) is obtained. Therefore, based on the time-series test data, it is possible to guide the health information so that the user can grasp his / her health condition by receiving it before it gets worse as he / she goes to the hospital. For example, in FIG.
  • the medical institution in the case of the test data at time T1, it can be inferred that the medical institution is visited at time Tc when time + ⁇ T has elapsed. That is, if the examination data, medical institution information (clinical name, clinical department, date and time information) and the like are accumulated in the DB unit 8, the period until receiving medical treatment at the medical institution can be estimated.
  • Fig. 3 (b) shows a case in which the patient has already visited the hospital and has deteriorated during the hospital visit due to factors other than treatment.
  • the graph shown in FIG. 3B is an example in which a person who goes to the hospital due to illness receives treatment at a clinic when specific information appears at times Tc1 and Tc2.
  • the time-series inspection data as shown in FIG. 3B can be sufficiently utilized to learn such a situation. This example is useful for a guide to the effect that "people with this number usually cannot be treated on their own.” It is effective as information that can prevent further deterioration.
  • Figure 3 (c) shows a case where it is not necessary to go to the clinic.
  • the test data D is lower than the predetermined value (indicated by the broken line in the graph) and there is no need to go to the clinic.
  • the column of the visit date and time is blank.
  • This database may hold the relationship between the type of acquired information (toilet bowl occult blood test information), the clinic, and the owned equipment Mod, and the patient-specific time-series data may be managed in a separate database. Further, by searching a plurality of DBs and organizing the search results, information corresponding to the database recorded in the DB unit 8 may be obtained.
  • FIG. 3 is a graph showing the time-series information for each patient recorded in the DB unit 8, where the horizontal axis is time and the vertical axis is the numerical value of the acquired information. Therefore, the information is two-dimensionally visual. Since it is a two-dimensional diagram, the following two things can be said. First, since it is a diagram, it can be handled in the same way as image judgment, and a general-purpose and easy-to-build AI chip or system such as an inference model for image recognition can be easily diverted, and inference can be easily realized. .. In addition, since the horizontal axis is time, it is possible to effectively use the time-varying information of physical information, and it is possible to easily make a prediction. In addition, since similar data is acquired repeatedly, the number of data is easy to collect, and even if there are some errors in individual data, the tendency is to compare the pattern with other people's data groups to make a judgment. It's easy to do.
  • this time axis it is possible to reflect information on the daily rhythm of daily life events, etc., which will be described later, as well as health and medical aspects that affect or reflect the health of the person. It is good to be able to reflect the event as well.
  • the time when you started exercising is an event on this time axis, and even if you feel better by starting to take some health food, you can check it in relation to this time axis. It can also be associated with changes in data.
  • FIGS. 3 (a), (b), and (c) it is simplified and illustrated in an easy-to-understand manner with an example of whether or not there was a hospital visit. It is possible to statistically judge the pattern change of the inspection data string and the influence of the relationship with these events.
  • inspection data is changed in a positive direction (for example, time-series data group of a corresponding person for a specific period, time-series inspection information, and health-related information is inspected.
  • the data may be added so that it can be identified), and the annotation "good data change" may be performed.
  • an examination data acquisition unit that acquires examination data for each profile
  • a storage unit that stores the medical institution that has been treated for each profile, examination data, profile information, and a medical institution for medical treatment. It has a transmission information determination unit that infers medical institution information and determines the transmission information to be transmitted to the target person by inputting the test data of a specific target person by the reasoning model learned from the information as teacher data. It becomes possible to provide an information transmission device characterized by.
  • the medical institution information used for the teacher data includes features such as the equipment, equipment, and medical records owned by the institution. If a medical institution with a specific facility is inferred, the information may be shared with that specific person, or the medical facility possessing the facility can be used to provide medical care with the device in the vicinity of the person's address. You may search for an institution and use it as information.
  • the time series information can include information on the characteristics of time changes peculiar to the living body, such as fluctuations and frequency. For example, it is easy for the user to consider information such as when sleeping, when waking up, morning and day and night, before meals, after meals, and before and after bathing. In addition, there is a study that it is more relaxed and healthier to have appropriate fluctuations in heart rate and breathing. If necessary, it can be used in various ways, such as extracting only the data at the time when it seems to be the timing as described above, omitting the data in a specific situation, and using only these data.
  • the period during which the history data is acquired corresponds to the specific period, and the inspection data during this specific period is extracted.
  • the extracted time-series inspection data is input to the inference engine 7, the inference engine 7 outputs advice by inference, and the advice is provided to the subject.
  • the specific time width may be suitable for giving some advice at a time corresponding to the future after the end of the time width, and a plurality of information retroactive to the time of this advice is acquired. It should be as wide as possible. Further, the specific time width may be standardized, or may not necessarily be strict, and it may be sufficient if a sufficient amount of data can be obtained.
  • time interval of each data is also important information, it is better that the data is obtained with a regular time width and is not discrete. However, even if the measurement time points of the data are discrete within the time width, it is effective as long as the time width is such that the data can be supplemented by interpolation to obtain meaningful data. It may be determined according to some health or medical information.
  • the information transmission device extracts the change pattern of the inspection data of the subject in a predetermined time width, and determines the transmission information to the subject according to the inference model learned together with the time information. It has a transmission information determination unit.
  • FIG. 4 shows a case where the search in the DB unit 8 shown in FIG. 1 and the inference by the inference engine 7 are individually used. You may want to use one of the functions, or you may use both of them in layers, but here is the simplest example.
  • an image sensor In explaining the flow of FIG. 4, as the information judgment device 2, an image sensor, a magnifying image judgment device such as a microscope, a sensor for detecting special light reflection, an array of crystalline nanowires, and a molecular film are used as the information judgment device 2.
  • the explanation will be made on the assumption that an olfactory sensor, a gas component sensor, etc. that apply changes in electrical characteristics such as the above are arranged and the characteristics of the user's excrement can be confirmed.
  • the recommended facility to be displayed is searched from the database recorded in the DB unit 8 (see S5Yes ⁇ S7).
  • the DB unit 8 is constructed with a database of information on facilities that can perform user examinations and treatments based on specific information.
  • the inference is performed using the user's history data (see S5No ⁇ S9).
  • each ID is determined based on the sensor output result (S1).
  • the control unit 1 may acquire the output of the information determination device 2 through the communication control unit 1a, or the control unit 1 may receive the data transmitted by the information acquisition device 2 in the communication control unit 1a. Further, it is assumed that the data recorded by the information determination device 2 is collected by the control unit 1 through the communication control unit 1a at a specific timing.
  • the inspection result is determined based on the sensor output for each ID attached to the sensor output result.
  • the sensors are a color sensor, a shape sensor, a hardness sensor, an olfactory sensor, a gas component sensor, and a color change detection sensor when a specific reagent is added. Based on the output of the image sensor, the shape is determined by a magnified observation image. May be good.
  • stool with occult blood can be determined by a color sensor.
  • the amount, shape, hardness, etc. of excretion may be determined by an image sensor / color sensor, or a method of measuring the color distribution by performing special dyeing may be used.
  • the composition may be detected in a magnified image of the object, or the result of culturing for a specific time may be determined. For example, when the amount of blood mixed in the stool increases, the red color of red blood cells becomes conspicuous, but if this is quantified, the difference from the healthy case can be seen. These are detected in step S1.
  • step S1 when the control unit 1 determines the sensor output result by a determination using a specific program or the like, it then determines whether or not specific information can be obtained (S3). Further, this specific information may be output by the information determination device 2 after determining that it is "specific information".
  • this specific information may be output by the information determination device 2 after determining that it is "specific information”.
  • it is determined whether or not specific information related to the disease for example, a feature such as a numerical value different from the healthy state is detected.
  • the user's profile, behavior, and lifestyle are determined (S15).
  • the profile, behavior, and lifestyle of the user using the terminal 4 are determined at the timing when the specific information is not acquired.
  • These information of the user may be input by the user from the terminal 4 through the communication control unit 1a by the control unit 1, and the user downloads a profile or the like uploaded on the net such as SNS and accumulates the information. May be good.
  • Such information transfer may be push type, pull type, frequent acquisition, or intermittent acquisition, and the control unit 1 acquires the manual input result of the user to the terminal 4 through the communication control unit 2.
  • it may be stored in the DB unit 8 so that it can be referred to.
  • the communication control unit 1a also intervenes in the communication with the DB unit 8.
  • advice on recommended facilities, etc. is obtained using the biometric information test data for each individual obtained in chronological order. Furthermore, the result of annotating the characteristic information of medical institutions, etc. that had an improvement effect on the change pattern information of the biometric information test data, or the result of annotating the input information of a specific health advice service is learned as teacher data. The case where the inference model is generated and the trigger information at the time of improvement of the inspection data is obtained by using this inference model is described. In addition, by including specific improvement timing information such as taking medication and starting life improvement as similar time information to the change pattern information, improvement of the test data of other people whose time series pattern of the biometric information test data is similar.
  • a facility or the like that satisfies the above extraction conditions may be searched.
  • advice is given to go to that particular medical institution.
  • the control unit 1 determines in cooperation with the communication control unit 1a whether or not a database suitable for searching / inferring using the specific information determined in step S3 is stored in the DB unit 8. For example, as a result of the determination in step S3, as a result of inspecting the color of the stool, when the numerical value representing red becomes larger than that in the case of health, a database suitable for determining such a health condition is stored in the DB section. It is determined whether or not it is accumulated in 8. Even if a suitable database is not stored in the DB unit 8 in the information transmission system including the control unit 1, it may exist in another system. Therefore, the database may be searched including other systems.
  • step S5 when the control unit 1 determines that the database exists in cooperation with the communication control unit 1a, the control unit 1 cooperates with the communication control unit 1a and is recorded in the DB unit 8.
  • Acquire related facility information including equipment (S7) what kind of temporal changes and transitions are made in the data output by the above-mentioned information determination device 2 of a specific patient candidate (test data, biological data, vital data, sample data, etc. are generally expressed as "time series data").
  • Search for people with similar patterns from the database (DB section 8) based on logic such as pattern matching, search for people who are consulting at a specific medical institution, and extract the characteristics of that medical institution. do it.
  • the above-mentioned database acquires inspection data in chronological order using an inspection device having a specific specification, acquires change pattern information of the inspection data, and responds to this change pattern information by taking medication or improving life.
  • specific improvement timing information such as start as time information similar to the time information of the change pattern information, it has information that makes it possible to compare the part of the change pattern before the improvement information.
  • an information transmission method characterized by transmitting improvement information to a specific person by comparing a part of a change pattern of a specific person with a part of a change pattern before the improvement information of another person. ..
  • the above-mentioned database is created by acquiring a plurality of inspection data in time series using an inspection device having a specific specification and a specific inspection data change pattern information. Each timing of a plurality of event information regarding the acquisition source of the inspection data within the period is recorded in association with each other. Using this database, it is possible to extract and provide information on events that have influenced the trend change from the event information based on the relationship between the trend change of the change pattern and the timing of the event.
  • step S5 it becomes possible to acquire related facility information and lifestyle information suitable for examining and treating a specific disease suspected from the specific information acquired in step S3.
  • the related facility information possessing the equipment used for inspecting and treating a specific disease is acquired, and this facility information is used as a candidate.
  • the above-mentioned stool is red, hemorrhoids or colon cancer are suspected, but since more detailed classification is possible based on the characteristics of color and the characteristics of time-series changes, for example, the anus. It will be possible to sort out and obtain information about facilities that have colonoscopies and can perform colonoscopy instead of departments.
  • step S5 If there is no database as a result of the determination in step S5, inference is made using historical data (S9). It is possible that a database suitable for the specific information acquired in step S3 is not prepared.
  • the historical data is input to the inference engine 7, and the inference engine 7 uses the inference model to infer the facilities and the like capable of appropriate inspection. This infers diseases related to specific information, and infers clinical departments / departments related to specific information. Then, based on this inference result, an examination facility (institution) / medical care facility (institution) suitable for the user's health condition is recommended. In addition to inferring the recommended medical facility, this step also infers the name of the user's disease and the time when the user's symptoms worsen and go to the clinic for treatment. The detailed operation of inference using this historical data will be described later with reference to FIG.
  • the recommended facility is narrowed down based on the user's profile and the like (S11).
  • the user goes to the hospital based on the profile information acquired in step S15, the information acquired from the related inspection institution 9, for example, the clinic information near the user, and the hospital information on the user's holiday. You may choose a convenient facility. This selection is performed by the control unit 1 cooperating with the search unit 1f to acquire information suitable for the conditions from the information stored in the DB unit 8.
  • step S13 is a step of providing information on examinations and medical assistance to users and related persons, and is displayed on the terminal 4. Further, depending on the health condition of the user, a warning display may be displayed.
  • the control unit 1 acquires the sensor detection results from the information determination device 2 and the like (S1), and is related to the health state (disease) from these detection results. It is determined whether or not there is specific information to be used (S3). When there is specific information, the database is searched to search for facilities suitable for examining and diagnosing the health condition (disease), including the owned equipment (S7). For this reason, the user can perform health checks in daily life, and can receive advice on facilities suitable for examinations and medical examinations, considering the facilities owned by the facilities according to the health condition. ..
  • the DB search (S7) and the inference (S9) are treated independently as separate processes.
  • the present invention is not limited to this, and these may be treated comprehensively.
  • step S9 of FIG. 4 the operation of "inference using historical data" in step S9 of FIG. 4 will be described using the flowchart shown in FIG.
  • the control unit 1 performs the operation in cooperation with the inference engine 7, the DB unit 8, and the like through the communication control unit 1a.
  • time series data is acquired (S21).
  • the time-series data corresponding to the specific ID recorded in the DB unit 8 is acquired (see FIG. 3 for the time-series data).
  • the time width of the time-series data to be acquired is set to a specific time width, but if the data of the specific time width cannot be acquired, it is set to the time range that can be acquired. This is because if there is no specific time width, the judgment will be based only on the data under a specific situation, and the reliability will be inferior.
  • the specific time span differs between diseases that progress over time, such as colorectal cancer, and diseases that progress in a short period of time, such as influenza.
  • the type of test data depends on the learning of the inference model, but it is desirable that the graph is a specific item used at the time of learning. For example, it is preferable not to infer body weight and blood pressure together.
  • step S23 After acquiring the time series data, it is next determined whether or not the change is remarkable (S23). Here, it is determined whether or not the change pattern of the acquired time series data is remarkable. If the result of this determination is significant, a warning is given (S25). If the value of the specific information itself deviates significantly from the standard value, or if the change pattern is remarkable, there is a high possibility that there is some kind of disease, so a warning is displayed to the user without executing the process of step S27. To do. This warning display may be displayed on the terminal 4, for example. When a warning is displayed, this flow is terminated and the original flow is restored.
  • step S27 it is determined whether or not the level has no problem.
  • the change pattern of the time series data acquired in step S21 is at a level at which there is no problem.
  • the level of the inspection data is equal to or lower than the predetermined level when viewed in a specific time width, it is determined that there is no problem.
  • the medical institution information is not output (S29). In particular, since there is no information to be displayed to the user, information about the medical institution is not output.
  • step S29 is executed, the flow returns to the original flow.
  • step S27 it is determined whether or not the time series data for a specific time width can be acquired (S31). For example, if the status of occult blood is detected, it is determined whether or not occult blood was obtained within a range of several months. That is, the specific time span depends on the disease involved.
  • the data is input to the inference unit and the inference result is acquired (S35). Only when the data of a specific time width can be acquired, the data is input to the inference engine 7 and the inference result is obtained.
  • the learning unit 5 can generate an inference model that can output an inference result such as "many people go to a clinic with a XX inspection device" including recommended equipment.
  • teacher data based on time-series data, whether or not it is necessary to go to the hospital, when it is necessary to go to the hospital, and which clinical department to go to.
  • Various inference models can be generated, such as whether to do so.
  • step S35 since the database organized as shown in FIG. 2 is used (used for annotation of the biological data change pattern), the medical institution is not simply output as the inference result. , Various features of the facility are output as inference results, and results that do not depend on individual medical institutions can be obtained.
  • the biological data change pattern used for learning receives medical treatment by selecting and learning the data before it is improved by going to the hospital, taking medication, etc. when the data of the person who has already visited the hospital is used as the teacher data. It will be easier to compare with the biometric data patterns of those who are wondering whether or not they are, or those who have no subjective symptoms and are not receiving medical treatment.
  • the database according to the embodiment of the present application acquires time-series change pattern information using an inspection device having a specific specification, and similarly performs specific improvement timing information such as taking medication or starting life improvement with respect to the change pattern information. By including it as time information, it has information that makes it possible to convert the change pattern before the improvement information into teacher data. Further, in the information transmission method in the embodiment of the present application, the change pattern of a specific person is input to the change pattern before the improvement information of another person, and the improvement information obtained as an inference result is transmitted to the specific person. ing.
  • step S31 if the data of the specific time width has not been acquired, no inference is performed (S33). Inference is possible depending on the expected reliability without information of a specific time width, but it can be difficult. Therefore, if it is determined in step S31 that the data of the specific time width has not been acquired, no inference is made. However, there are cases where a clearly dangerous situation can be detected, in which case emergency information may be output before inference.
  • the acquired data is a numerical value that has a significant problem
  • there is no time grace to make a long-term forecast by inference and it will be dealt with in steps S23 and S25.
  • This response prevents the problem of not being able to respond to an emergency without making inferences in step S33, and makes it possible to create a highly reliable system in which information is output after sufficient data has been collected. That is, in the present embodiment, in the case of a numerical change that is contained in a specific change, the change pattern of the test data of the subject is cut out within a predetermined time width, and inference is performed according to the inference model learned together with the time information. When the inference result is obtained, this flow is terminated and the original flow is restored.
  • the level of the time series data is at a level that does not cause any problem, and if there is no problem, no information is output (S29).
  • the time series data is at a level where there is no problem, it is possible to refrain from outputting the information in order to prevent unnecessary inference. That is, when there is a disease having a cause other than the change in biological information, the possibility of outputting an unrelated inference result when this information is included in the teacher data is excluded. That is, in the present embodiment, when the specific criteria are not satisfied (for example, S23Yes, S27Yes), the inference model learned together with the time information by cutting out the change pattern of the inspection data of the subject within a predetermined time width. No inference is made according to. We expect the learning effect to be excellent in inferring something from subtle changes that cannot be visually detected by humans, and eliminate the harmful effects of making too subtle inferences.
  • the inference by the inference engine 7 is executed in step S35.
  • the determination in steps S23 and S27 may be made by the inference engine 7. In this case, it may be executed using the same inference model, or it may be executed by different inference engines.
  • step S7 of FIG. 4 the database was simply searched.
  • the acquired time-series data had to be related to a specific clinical department, but in reality, information not related to a specific clinical department may be required. For example, gaining weight or increasing blood pressure may require tests other than specific departments or specific devices.
  • related facility information can be appropriately acquired even in such a case.
  • control unit 1 When the control unit 1 starts the operation of the flow shown in FIG. 6 in cooperation with the communication control unit 1a, it first determines whether or not the information is for a specific clinical department (S41). Here, the control unit 1 determines whether or not the time-series data to be acquired is information for a specific clinical department.
  • step S41 if it is not for a specific clinical department, in steps S43 to S47, in addition to the data recorded in the DB unit 8, a plurality of information (data) transitions are used to be appropriate.
  • the control unit 1 extracts a change pattern of the inspection data of the subject within a predetermined time width, and extracts the data that changes with time.
  • An inference model is generated by learning using this inference model, inference is performed using this inference model, and information is output by referring to the inference result and the possessed device database.
  • the inference model used here includes data with time information (see Dy (t) in FIG. 2) as shown in FIGS. 3 (a) to 3 (c) during learning and the clinical department received by the user. It is assumed that it is learned as an output.
  • step S43 time series data is acquired (S43).
  • the control unit 1 acquires the past data from the information determination device 2 and the like as a history. These data are recorded in the memory in the control unit 1 or the DB unit 8.
  • the acquired time series data is data for a specific time width (S45).
  • a general clinic is recommended (S53). Since the time width of the time series data does not have a specific time, it is not possible to infer related diseases and refer to a specific facility, so a general medical facility is recommended. Note that the medical institution information may not be output as in S29 of FIG.
  • the data is input to the inference unit and the related disease information is acquired (S47).
  • the control unit 1 inputs the acquired data to the inference engine 7 and infers the related disease information.
  • the inference model used at this time generates information as shown in FIG. 3 as teacher data.
  • step S47 If the related disease information is acquired in step S47, or as a result of the determination in step S41, the information is for a specific clinical department, then the related equipment is determined (S49), and the equipment possession facility information (clinical department) is determined. ) Is acquired (S51).
  • the control unit 1 determines what the specific clinical department determined in step S41 or the equipment related to the disease acquired in step S47 is. Then, the control unit 1 searches for and selects a facility having the determined equipment from the facility-specific equipment list 8a of the DB unit 8.
  • each ID is determined based on the sensor output result (S1).
  • the control unit 1 determines the inspection result for each ID attached to the sensor output result based on the sensor output.
  • the specific information related to the disease for example, a feature such as a numerical value different from the healthy state, based on the determination in step S1. judge. If the specific information cannot be obtained as a result of the determination in step S3, the user's profile, behavior, and lifestyle are determined in the same manner as in the flow of FIG. 4 (S15).
  • the process returns to step S1.
  • step S4 If specific information can be obtained as a result of the determination in step S3, it is determined whether or not there is an inference model (S4).
  • the inference model specification determination unit 1d in the control unit 1 determines the inference model specifications for giving advice (display of recommended facilities, etc.) to the target person, and the inference request unit 1e generates an inference model for the learning unit 5. To ask.
  • the inference model generated by the learning unit 5 is received, it is set in the inference model 7.
  • a plurality of inference models are created so as to correspond to various situations, and are stored in the control unit 1 or the inference engine 7.
  • this step 4 whether or not an inference model capable of giving appropriate advice (display of recommended facilities, etc.) to the target person for the specific information acquired in step S3 is stored in the control unit 1 or the like. To judge.
  • step S4 If there is an inference model as a result of the determination in step S4, inference is performed using the historical data (S6).
  • the inference model selected in step S4 is set in the inference engine 7.
  • the historical data of the target person (see the time-series data in FIG. 2) transmitted from the information determination device 2 and recorded in the DB 8 is input to the input layer of the inference engine 7, and inference is performed.
  • the inference result is output from the output layer.
  • the inference in step 6 may be performed by executing the flow shown in FIG.
  • the inference result is acquired in step 6, it is next determined whether or not the inference result is highly reliable (S8).
  • the reliability of the inference result in step S6 is calculated, and it is determined whether or not this reliability is higher than the predetermined value.
  • the high reliability may be inferred using data prepared in advance, and the degree of deviation from this result may be calculated.
  • step S10 If the result of the determination in step S8 is low reliability, or if the result of the determination in step S4 is that there is no inference model, training data is collected and an inference model is created (S10).
  • the control unit 1 collects the specific information and similar data acquired in step S3.
  • various databases such as a large number of DB units 8 connected on the Internet and databases in related inspection institutions 9 and the like are searched, and the above-mentioned similar data is collected.
  • the data shows the name of the disease, test items, and the like.
  • time-series data that shows what kind of biological information it is (or what kind of specifications and properties it is measured by measuring devices, sensors, etc.), and also has signs and precursors of assumed diseases. It is preferable that the number of data and the acquisition time range are sufficient to read the time-series pattern that indicates that the patient has a disease.
  • teacher data is created by associating the output result with the input data for each similar data.
  • annotation information for this pattern information such as whether or not there is information on the medical institution that led to the medical examination (more preferable if it includes information such as the clinical department and the equipment and fixtures there). I'm assuming. Teacher data generated using these patterns and annotation information is collected.
  • the control unit 1 requests the learning unit 5 to create an inference model through the inference request unit 1e.
  • the inference model learned from this teacher data is highly reliable and can be used to determine whether a person with that data pattern can maintain good health, see a doctor, and so on.
  • the reliability of the existing inference model is low, a new inference model is recreated from the teacher data, so that highly reliable inference can always be performed.
  • the learning unit 5 creates an inference model, it sends it to the control unit 1.
  • the control unit 1 receives the inference model, it executes steps S4 to S8 using this inference model.
  • the inference model for inferring the time when the user goes to the medical facility for medical examination, etc. In this user's state, it may end up going to the medical facility, but to avoid going. You may generate an inference model that advises you on how to deal with it. For example, if there is a description in the SNS or blog on the Internet that you are likely to get sick but have been treated, you may collect this information. In addition, if the medical database or the like contains information such as treatment for a person who is likely to get sick, such information may be collected. If information can be collected, learning is performed using this information and an inference model is generated.
  • the related inspection organization 9 shown in FIG. 1 is based on a technique of deriving and presenting information that contributes to the recovery and maintenance of the user's health by selecting and describing health-related information classified into a specific category.
  • the writing and selection information input by the person should be annotated together with the historical data of the specific target person (see the time series data in FIG. 2) as the teacher data.
  • the teacher data For example, it is possible to convert the inference model into teacher data.
  • doctors and medical professionals may also use the services described above to refer to the diagnosis, so it is used by doctors at the time of medical examination / diagnosis performed by doctors at the time of medical examination of a specific patient.
  • the manual input result to an information terminal such as a PC or the voice input result may be used.
  • the patient's history data (see the time-series data in FIG. 2) is acquired, and the above-mentioned input result is added to this history data as annotation information so that which item corresponds to what kind of input information can be identified. I will go.
  • the patient history data does not necessarily have to be the history data of one item, and may be used by incorporating information on lifestyle habits, hospital visits, medication information, etc. in chronological order.
  • Using the teacher data obtained in this way we train to obtain reliable results and create a new inference model.
  • inspection data is acquired in time series using an inspection device having a specific specification
  • change pattern information of the inspection data is acquired
  • a specific health advice service is provided for this change pattern information.
  • the inference model in which the result of annotating the input information of is trained as teacher data, the result of inferring by inputting the inspection data obtained in time series using the inspection equipment of the specific specifications of a specific person is specified. It becomes possible to provide a method of transmitting information to the person in question.
  • this information transmission method first responds to the input of a specific health response event, and prior to the health response event, uses a specific specification inspection device to respond to the specific health response event in chronological order.
  • the inspection data is acquired and the time-series inspection data is recorded.
  • the result of annotating the change pattern information of the recorded test data with the information of the equipment, equipment, and environment at the time of the health response event is created as teacher data.
  • learning is performed and an inference model is generated.
  • inspection data is acquired in chronological order using an inspection device with specific specifications, and the acquired inspection data is input to an inference model to infer information on equipment, equipment, and the environment. From this inference result, a health response event customized for a specific person is transmitted to a specific person.
  • the health response event is an event related to health, for example, an event such as receiving a medical examination by a doctor at a medical facility.
  • step S8 If the result of the determination in step S8 is high reliability, then the recommended facility is displayed based on the inference result (S13a).
  • the recommended facility is displayed on the terminal 4 based on the inference result in step S6.
  • the medical facilities and inspection institutions that can carry out this inspection may be searched for the facility-specific possessed equipment list 8a in the DB8, and the search results may be displayed. Further, in step S6, if it is possible to infer a medical facility or an institution that can be inspected, the recommended facility may be displayed based on the inference result.
  • step S13a advice on recommended facilities, etc. is obtained using the biometric information test data for each individual obtained in chronological order. Furthermore, the result of annotating the characteristic information of medical institutions, etc. that had an improvement effect on the change pattern information of the biometric information test data, or the result of annotating the input information of a specific health advice service is learned as teacher data. The method of generating the inference model and using this inference model to obtain the trigger information when improving the inspection data is explained.
  • the information that triggers the improvement of the test data obtained here is information that has been reduced to the level of the test or treatment, the life pattern and life of the person who transmitted the information (the person who receives the information). It is desirable to devise ways to convert and present advice so that information that can be used is transmitted even in the regions and regions where it is used. By searching for information that meets the conditions in light of the lives of the people who receive the information, it will be possible to obtain information that can be used in other regions as well. In addition to the living area of the person who receives the information or the area adjacent to it, it is sufficient to search for facilities that satisfy the extraction conditions and convey the corresponding items by display or the like.
  • step S13a When the recommended facility is displayed in step S13a, the process returns to step S1.
  • inference is performed using an inference model in various steps (for example, S9 in FIG. 4, S35 in FIG. 5, and S6 in FIG. 7).
  • the inference model can also input patterns of time-series changes in biometric information to infer future health risks and their remedies, or to infer how far in the future it is.
  • the creation of such an inference model will be specifically described with reference to FIGS. 8A and 8B.
  • FIG. 8A shows the temporal change of the health state
  • the horizontal axis shows the change of time T
  • the vertical axis shows the biological data D.
  • the time-series data Dts11 and Dts12 in which the black circles in FIG. 8A are connected indicate the case where the health condition deteriorates due to some trigger (specific event), and the time-series data Dts21 and Dts22 in which the white circles are connected are the triggers (specification).
  • the event shows when the health condition is improved.
  • the health condition is improved or deteriorated by some trigger (specific event; timing is indicated by the trigger information Inf), the health condition appears in the biological data D, and the trend of the pattern changes.
  • the vertical line shown by the dotted line parallel to the trigger information Inf is an example of another event.
  • an increase in biological data D indicates a deviation from the health state
  • a decrease in biological data D indicates recovery or improvement. For example, eating something bad (this is an event) and having a fever, or having a medical examination and taking medicine (this is an event) and recovering can be familiar, but in many cases what is the trigger. There are few patients who do not know or are aware of how they have recovered numerically.
  • time-series data Dts21 and Dts22 are different numerical values, and those that have decreased due to changes in health status (for example, muscle strength, bone density, and visual acuity due to aging) (Dts21) are events such as going to a hospital and receiving treatment. (Inf timing) may be taken as an example showing how it has recovered.
  • FIG. 8B By executing such a flowchart, it is possible to identify an event of what affects health and how.
  • the flow shown in FIG. 8B is executed by controlling each unit in the control unit 1 by a CPU or the like in the control unit 1, but is not limited to the control unit 1 and may be executed by another server, engine, or the like. ..
  • the event information corresponding to the time series data acquisition period is acquired (S61).
  • the time-series data acquisition period varies depending on the target disease, health condition, etc., but here, it may be determined in consideration of the period in which the event generally affects the health.
  • the event information is acquired from various sources such as a schedule recorded on the user's terminal 4 and a schedule uploaded to SNS or the like.
  • inspection data is acquired in a specific time series using an inspection device having specific specifications.
  • the event was attended by the subject, such as going to a hospital for medical examination, purchasing medicine at a pharmacy and taking it, or going to a cold and crowded place. Actions and behaviors that may affect your health.
  • step S63 the trend change of the pattern before and after the specific event is compared, and it is determined whether or not there is a difference (S63).
  • the specific event information is acquired in step S61, it is determined whether or not there is a change in the trend of the time series data (biological data) before and after the specific event. That is, the change pattern information of the inspection data is acquired, and it is determined whether or not the pattern of the time series data before the specific event and the trend (trend) of the time series data after the specific event are the same.
  • step S63 If there is no difference in the trend change as a result of the determination in step S63, the determination is made using another event (S75). In this case, since there is no difference in the trend change before and after the event in step S63, it can be said that the health state does not change before and after the event. Therefore, the process returns to step S63, and an event with a trend change is searched for.
  • step S65 the trend of the data change before and after the trigger information (Inf in FIG. 8A) is classified (S65).
  • Trigger information You can see whether the event affected your health when there is no difference in the trend change before and after. However, since it may have a good effect or a bad effect, in this step S65, it is determined whether the trend is a good trend or a bad trend, and the trend of the data change pattern is classified based on the determination result.
  • step S65 the classification result in step S65 is classified into an improved event or a worsened event, and the environment, components, and the like of the event are used as annotation information.
  • the environment and components are the decomposition of an event, such as simply going to a crowded place on a cold day, instead of simply going to Tokyo Station.
  • the event "I went to the hospital and had a medical examination” was simply held at an event such as "I took an X-ray", “I injected XX", "I took ⁇ ", etc. It is a disassembled version of. That is, in step S67, the event information is decomposed for each configuration and the components are extracted. By extracting the components and the like, creating teacher data, and learning, it is possible to provide customized information based on the extracted components that have affected the trend change.
  • the pattern showing the trend change before or after the specific event is converted into teacher data (S69).
  • the good event and the bad event are annotated with respect to the time-series information pattern indicating the trend change before the specific event and converted into teacher data.
  • step S71 the learning / inference model is created (S71).
  • learning is performed using the teacher data created in step S69 to generate an inference model. That is, when the trend changes, there is an event that triggers it, so when time series data is input, an inference model that can output a specific event (impact event) is generated.
  • step S69 if the teacher data is generated using the pattern before the specific event, it is possible to generate an inference model capable of inferring that the presence of the specific event makes it worse (in this way).
  • teacher data is generated using the pattern after a specific event, it is possible to generate an inference model that can make an inference that if there is the specific event, it will be improved (in this way).
  • this step S71 with respect to the change pattern information, the characteristic information of the medical institution or the like having the improvement effect and the influence event information at the time corresponding to the time when the conversion pattern changes within a specific period are detected and detected.
  • an inference model When an inference model is generated, it is updated at a specific timing (S73). Biological data and event information are accumulated from moment to moment, so they are updated regularly or at the time of the weather, news, etc. If a specific data pattern is input to the inference model generated in step S71, it is possible to give advice that can be improved or deteriorated in this event in the future, so that it is necessary to be aware of it. For seasonal changes and diseases that are prone to infection, it is better to use information such as moving to congested areas as an event. This may be added in step S73 so as to correspond to the time axis of the time series data. You can also create an inference model that determines what went wrong with data changes after the event.
  • the flow of creating the inference model is a step of determining an event that affects the change pattern of the time-series data of the biological information representing the health state (S63) and a step of determining whether the event has an improvement effect (S63).
  • An information providing method including S67) and a step (S69) of determining an event having an effect of improving a change pattern according to a time-series pattern before the time of the event is shown. That is, it is possible to discover events that affect health based on the post-event pattern.
  • the recording system in the present embodiment acquires a plurality of inspection data in chronological order over a specific period using a (biological data) inspection device having a specific specification, and creates a data change pattern.
  • a plurality of event information regarding the data acquisition source within this specific period can be recorded in association with each other up to the timing of when it occurred.
  • This recording system may include a database for recording data and events. Further, this database may be a plurality of linked computers. Since this recording system should be able to extract the events that affected the trend change, it provides useful information to other people by making it a circuit, program, or system that can provide information on the events that have affected this trend. sell.
  • the event information is subdivided based on the environment and situation where the event occurred and other component information obtained, it can be provided as customized information replaced by the position of the person who provides the information. For example, if you break it down into factors such as going to a crowd in the area where the outbreak of an infectious disease was reported, it can be generalized from the actual specific behavioral information that you went to the ferry landing, so don't go to a crowded place today. It is converted into information such as.
  • the inspection data change pattern and the teacher data of many cases obtained by annotation of these influence events are collected, learned and an inference model is created, the change pattern is input to the inference model, and the output is the influence event element. You may try to.
  • inspection data is acquired in a specific time series using an inspection device having specific specifications (see, for example, S61 in FIG. 8B).
  • the change pattern information of the test data was acquired (for example, see S63 in FIG. 8 (b)), and the characteristic information of the medical institution etc. that had an improvement effect on the change pattern information and the conversion pattern changed within a specific period.
  • the inspection data obtained in time series in a period similar to the width of the specific period is input to the inference model to infer the influence event, and the inference is performed.
  • the results are transmitted (for example, in S9 in FIG. 4, S35 in FIG. 5, and S6 in FIG. 7, it can be inferred using the above-mentioned inference model).
  • inspection data is acquired and recorded in time series using an inspection device having a specific specification prior to the health response event.
  • An inference model is acquired in which the result of annotating the equipment, equipment, and environment information at the time of the health response event is learned as teacher data for the change pattern information of the test data (for example,). See S71 in FIG. 8 (b)).
  • the inspection data obtained in time series using the inspection device of the specific specification of the specific person is input to the above-mentioned inference model and inferred, and the inference result is transmitted to the specific person (for example).
  • S9 in FIG. 4, S35 in FIG. 5, and S6 in FIG. 7 can be inferred using the above-mentioned inference model).
  • inspection data is acquired in time series using an inspection device having specific specifications, and change pattern information of the inspection data is acquired (see, for example, S1 in FIG. 4).
  • change pattern information such as taking medication and starting life improvement as the same time information for the change pattern information
  • a database having information that makes it possible to compare the change patterns before the improvement information is created.
  • the change pattern of a specific person is compared with the change pattern of another person before the improvement information, the improvement information is searched, and the searched improvement information is transmitted to a specific person (for example).
  • information can be transmitted using the search results of the database).
  • change pattern information of inspection data created by acquiring a plurality of inspection data in a time series using an inspection device having a specific specification and a specific inspection data.
  • event information it is possible to provide information on events that have influenced the trend change (see, for example, FIGS. 8A and 8B).
  • a database may be searched, or an inference model may be generated and inferred using this inference model.
  • various events such as impact events and health response events are described, but these are also included in the broad sense of the event.
  • the inspection data of the subject is acquired (for example, see S1 in FIG. 7), and it is determined whether or not the inspection data is specific input data (for example, in FIG. 7). (See S3), and if the inspection data is specific input data as a result of this determination, inference is performed using a specific inference model (see, for example, S4Yes ⁇ S6 in FIG. 7), and the specific input data is subjected to inference.
  • a specific inference model see, for example, S4Yes ⁇ S6 in FIG. 7
  • training is performed to generate an inference model by newly collecting input data similar to the specific input data and using it as teacher data (for example).
  • the inspection data of the subject is input (for example, refer to the information determination device 2 in FIG. 1, S1 in FIG. 4, S21 in FIG. 5 and the like), and the profile information of the subject and the profile information of the subject.
  • the possessed device information for each test / medical institution see, for example, DB section 8 in FIG. 1, S7 in FIG. 4 and the like
  • the transmission information to be transmitted to the target person is determined (see, for example, the information providing unit 1c in FIG. 1, S11 in FIG. 4 and the like).
  • the information can be transmitted to the target person in consideration of the equipment owned by the examination / medical institution. Further, in the present embodiment, it is possible to introduce an effective facility based on the health-related information obtained unconsciously on a daily basis, and it is possible to save the trouble of visiting the facility many times.
  • the test data of the subject is input (see, for example, the information determination device 2 in FIG. 1, S21 in FIG. 5 and the like), and the test of the subject / visit to a medical institution / inspection.
  • the inference unit that has an inference model learned according to the medication information
  • the change pattern of the test data of the subject is input and inference is performed, and the transmission information to be transmitted to the subject is determined based on the inference result.
  • the information providing unit 1c of FIG. 1, the inference engine 7, FIG. 3 (a), S35 of FIG. 5 and the like Therefore, it is possible to notify the subject by predicting the time of visit to a medical institution or the like in advance from the time-series change of the examination data. It will be possible to receive appropriate tests and treatment before the symptoms worsen.
  • the information transmission program includes a step of inputting test data of a subject (see, for example, information determination device 2 in FIG. 1, S21 in FIG. 5 and the like), and inspection / medical treatment of the subject.
  • test results and test results are stored in a database and the test results are used, there is no description about notifying the optimal medical institution based on the test results.
  • proposals to ask medical personnel in remote areas to observe excrement there is no proposal to inform the optimal medical institution.
  • proposals to display a history browser there is no proposal to announce the optimal facility for examining the symptoms of a subject based on the equipment owned by a medical institution or the like.
  • Many general users unknowingly install sensors, surveillance cameras, watching cameras, home thermometers, weight scales, and bodies in sanitary facilities such as mobile terminals, home appliances, baths, toilets, and washrooms in their daily lives.
  • Biological information may be monitored in chronological order with a composition meter, sphygmomanometer, or the like.
  • daily monitoring is performed, information obtained by the monitoring is actively utilized, and advice is provided so that a detailed examination can be performed at an appropriate facility based on biological information and sample information.
  • the information determination device 2 may be any device for acquiring health-related information of the target person, for example, vital information, sample information, and the like. In the simplest example, it can be applied to face image information obtained from a mobile terminal such as a smartphone, heartbeat information based on the face image information, and the like, and these information may be utilized. In addition, it may be linked with a device such as a wearable terminal that is used in close contact with the user, and data to be noted such as arrhythmia can be easily acquired by these devices.
  • a history pattern that includes multiple data instead of analyzing single-shot data that may contain errors depending on the equipment, physical condition, eating and drinking, and living scenes, the presence or absence of illness, possibility, recovery, and outpatient visits Information such as when to do it, advice information, etc. can be provided with high accuracy. If this information is of low accuracy, the user will be delayed in seeing the doctor and will be more concerned than necessary.
  • control unit 1 has been described as an IT device composed of a CPU, a memory, an HDD, and the like.
  • part or all of each part may be configured in a hardware circuit, and a gate generated based on the program language described by Verilog.
  • a hardware configuration such as a circuit may be used, or a hardware configuration using software such as a DSP (Digital Signal Processor) may be used.
  • DSP Digital Signal Processor
  • the element is not limited to the CPU, and may be any element that functions as a controller, and the processing of each part described above may be performed by one or more processors configured as hardware.
  • each part may be a processor each of which is configured as an electronic circuit, or may be each circuit part of a processor composed of an integrated circuit such as an FPGA (Field Programmable Gate Array).
  • a processor composed of one or more CPUs may execute the functions of each unit by reading and executing the computer program recorded on the recording medium.
  • the controls mainly described in the flowchart can often be set by a program, and may be stored in a recording medium or a recording unit.
  • the recording method to the recording medium and the recording unit may be recorded at the time of product shipment, the distributed recording medium may be used, or may be downloaded via the Internet.
  • the operation in the present embodiment has been described using a flowchart, but the order of the processing procedures may be changed, or any step may be omitted. Steps may be added, and specific processing contents in each step may be changed.
  • the present invention is not limited to the above embodiment as it is, and at the implementation stage, the components can be modified and embodied within a range that does not deviate from the gist thereof.
  • various inventions can be formed by an appropriate combination of the plurality of components disclosed in the above-described embodiment. For example, some components of all the components shown in the embodiment may be deleted. In addition, components across different embodiments may be combined as appropriate.

Landscapes

  • Medical Informatics (AREA)
  • Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Biomedical Technology (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

L'objectif de la présente invention est de présenter un événement relatif à un changement de l'état de santé d'un utilisateur. Selon la présente invention : des données d'examen sont acquises sous la forme d'une série chronologique dans une période de temps spécifiée, à l'aide d'un dispositif d'examen ayant une spécification spécifiée (S61) ; des informations de motif de variation concernant les données d'examen sont acquises (S63) ; des informations d'événement d'influence à un instant correspondant à l'instant auquel le motif de variation a varié dans la période de temps spécifiée est détectée par rapport aux informations de motif de variation, et un modèle d'inférence qui a été enseigné à l'aide, en tant que données d'enseignant, de résultats obtenus par l'annotation des informations d'événement d'influence détectées, est acquis (S65 à S71) ; un événement d'influence est inféré par entrée dans les données d'examen de modèle d'inférence d'une personne spécifiée, obtenues sous la forme d'une série chronologique dans une période de temps ayant une largeur similaire à la période de temps spécifiée, à l'aide de la machine d'inspection ayant la spécification spécifiée ; et le résultat d'influence est transmis.
PCT/JP2020/041125 2020-01-10 2020-11-02 Dispositif et procédé de transmission d'informations WO2021140731A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202080085581.7A CN114868203A (zh) 2020-01-10 2020-11-02 信息传递装置以及信息传递方法

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
PCT/JP2020/000763 WO2021140670A1 (fr) 2020-01-10 2020-01-10 Dispositif de transmission d'informations et procédé de transmission d'informations
JPPCT/JP2020/000763 2020-01-10

Publications (1)

Publication Number Publication Date
WO2021140731A1 true WO2021140731A1 (fr) 2021-07-15

Family

ID=76787834

Family Applications (2)

Application Number Title Priority Date Filing Date
PCT/JP2020/000763 WO2021140670A1 (fr) 2020-01-10 2020-01-10 Dispositif de transmission d'informations et procédé de transmission d'informations
PCT/JP2020/041125 WO2021140731A1 (fr) 2020-01-10 2020-11-02 Dispositif et procédé de transmission d'informations

Family Applications Before (1)

Application Number Title Priority Date Filing Date
PCT/JP2020/000763 WO2021140670A1 (fr) 2020-01-10 2020-01-10 Dispositif de transmission d'informations et procédé de transmission d'informations

Country Status (2)

Country Link
CN (1) CN114868203A (fr)
WO (2) WO2021140670A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2023035988A (ja) * 2021-08-30 2023-03-13 アップル インコーポレイテッド 体組成分析回路を有する電子デバイス
WO2023157596A1 (fr) * 2022-02-15 2023-08-24 ソニーグループ株式会社 Procédé de traitement d'informations, dispositif de traitement d'informations, programme, et système de traitement d'informations

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116206774B (zh) * 2023-04-27 2023-07-14 深圳市浩然盈科通讯科技有限公司 一种结合大数据自动匹配护理治疗方案的方法及系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009080700A (ja) * 2007-09-26 2009-04-16 Toshiba Corp 健康医療情報管理分析装置
JP2016218536A (ja) * 2015-05-15 2016-12-22 コニカミノルタ株式会社 診療支援システム、サーバー装置及びプログラム
WO2017037946A1 (fr) * 2015-09-04 2017-03-09 株式会社日立システムズ Système et procédé de service d'assistance pour la gestion de mode de vie
JP2018147407A (ja) * 2017-03-08 2018-09-20 オムロン株式会社 要因推定装置、要因推定システム、および要因推定方法
JP6566409B1 (ja) * 2018-08-24 2019-08-28 株式会社鈴康 情報処理装置、プログラム及び情報処理方法

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH1189822A (ja) * 1997-09-16 1999-04-06 Forward Inku:Kk 在宅血液検査の方法
US20040122704A1 (en) * 2002-12-18 2004-06-24 Sabol John M. Integrated medical knowledge base interface system and method
JP5631635B2 (ja) * 2010-06-03 2014-11-26 オリンパスメディカルシステムズ株式会社 医療業務支援装置
JP6494935B2 (ja) * 2014-07-09 2019-04-03 キヤノンメディカルシステムズ株式会社 検査情報提供装置
US11640852B2 (en) * 2015-04-08 2023-05-02 Koninklijke Philips N.V. System for laboratory values automated analysis and risk notification in intensive care unit
US20200126664A1 (en) * 2017-07-24 2020-04-23 Axion Research Inc. Support system for estimating an internal state of a target system
JP6804489B2 (ja) * 2018-05-31 2020-12-23 キユーピー株式会社 情報提供装置及び情報提供方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009080700A (ja) * 2007-09-26 2009-04-16 Toshiba Corp 健康医療情報管理分析装置
JP2016218536A (ja) * 2015-05-15 2016-12-22 コニカミノルタ株式会社 診療支援システム、サーバー装置及びプログラム
WO2017037946A1 (fr) * 2015-09-04 2017-03-09 株式会社日立システムズ Système et procédé de service d'assistance pour la gestion de mode de vie
JP2018147407A (ja) * 2017-03-08 2018-09-20 オムロン株式会社 要因推定装置、要因推定システム、および要因推定方法
JP6566409B1 (ja) * 2018-08-24 2019-08-28 株式会社鈴康 情報処理装置、プログラム及び情報処理方法

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2023035988A (ja) * 2021-08-30 2023-03-13 アップル インコーポレイテッド 体組成分析回路を有する電子デバイス
WO2023157596A1 (fr) * 2022-02-15 2023-08-24 ソニーグループ株式会社 Procédé de traitement d'informations, dispositif de traitement d'informations, programme, et système de traitement d'informations

Also Published As

Publication number Publication date
CN114868203A (zh) 2022-08-05
WO2021140670A1 (fr) 2021-07-15

Similar Documents

Publication Publication Date Title
CN105260588B (zh) 一种健康守护机器人系统及其数据处理方法
Ball et al. Improving diagnosis in health care
Peleg et al. MobiGuide: a personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains
WO2021140731A1 (fr) Dispositif et procédé de transmission d'informations
JP7190859B2 (ja) 健康情報処理方法、健康情報処理装置、コンピュータプログラム、及び学習モデル
KR101141425B1 (ko) 온라인을 통한 개인 맞춤형 건강관리와 진료방법 및 온라인 건강관리와 의료서비스 제공 서버장치
WO2005122033A1 (fr) Appareil d’information totale médicale et système d’information totale médicale
US20060112050A1 (en) Systems and methods for adaptive medical decision support
US20190287661A1 (en) Related systems and method for correlating medical data and diagnostic and health treatment follow-up conditions of patients monitored in real-time
EP3547320A2 (fr) Système et procédés associés pour réaliser une corrélation entre des données médicales et des conditions de diagnostic et d'accompagnement du traitement de santé d'un patient surveillé en temps réel
KR100737382B1 (ko) 의료 진단 데이터 연산방법
JP2013191021A (ja) 健康診断情報提供装置及び健康診断情報提供方法
JP2020135401A (ja) ソフトウェア及び診断支援装置
AU7719501A (en) Online medical evaluation and treatment system, method and portal
CN116543917A (zh) 一种针对异构时间序列数据的信息挖掘方法
KR20070106942A (ko) 의료 예상 진단 서비스 방법
WO2021152710A1 (fr) Dispositif de transmission d'informations et procédé de transmission d'informations
US20090070145A1 (en) Method and system for coronary artery disease care
Hasman My journey through the field of medical informatics
WO2021176622A1 (fr) Dispositif de détermination de capteur et procédé de détermination de capteur
WO2021181634A1 (fr) Dispositif de demande de collecte de données d'enseignant et procédé de collecte de données d'enseignant
Hine et al. Decision making by emergency room physicians and residents: implications for the design of clinical decision support systems
WO2021171596A1 (fr) Système d'inférence, dispositif de demande d'apprentissage, et procédé de demande d'apprentissage
Sabbir et al. Web-based health monitoring system and textual mining
WO2022244265A1 (fr) Serveur de service de guide d'examen et procédé de guide d'examen

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20912926

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20912926

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: JP