Health support system, server device, health support method, and program

Abstract

To accurately assess the risk of the target disease and encourage appropriate behavioral changes. [Solution] The health support system comprises a prediction processing unit that predicts the disease risk of a target disease from the user's medical history information based on a trained model generated by performing machine learning using the past medical history information of multiple people as training data, which predicts the disease risk, which is the risk of developing and worsening of a target disease; and an intervention processing unit that classifies the risk group of the target disease based on the disease risk predicted by the prediction processing unit, and executes an approach process to reduce the disease risk for the user according to the classified risk group of the target disease.

Description

Technical Field

[0006] , , , ,

[0001] The present invention relates to a health support system, a server device, a health support method, and a program.

Background Art

[0002] In recent years, a system for statistically examining the superiority and inferiority of intervention methods using electronic medical records for medical care and nursing care has been known (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the conventional systems as described above, the evaluation of group classification by intervention method is performed to search for intervention methods. For example, it is difficult to accurately determine the risk of a target disease and encourage behavior modification for high-risk individuals of the target disease. In addition, there is a problem that general medical examination recommendations from local governments and the like are unlikely to lead to behavior modification of high-risk individuals.

[0005] The present invention has been made to solve the above problems, and an object thereof is to provide a health support system, a server device, a health support method, and a program that can accurately determine the risk of a target disease and encourage appropriate behavior modification.

Means for Solving the Problems

[0007] Furthermore, one aspect of the present invention is a server device comprising: a prediction processing unit that predicts the disease risk of a target disease from a user's medical history information based on a trained model generated by performing machine learning using the past medical history information of multiple persons as training data, which predicts the disease risk, which is the risk of developing and worsening of the target disease; and an intervention processing unit that classifies the risk groups of the target disease based on the disease risk predicted by the prediction processing unit, and executes an approach process to reduce the disease risk for the user according to the classified risk group of the target disease.

[0008] Furthermore, one aspect of the present invention is a health support method comprising: a prediction processing step in which a prediction processing unit predicts the disease risk of a target disease from a user's medical history information based on a trained model generated by performing machine learning using the past medical history information of multiple persons as training data, which predicts disease risk, which is the risk of developing and worsening of a target disease; and an intervention processing step in which an intervention processing unit classifies the risk groups of the target disease based on the disease risk predicted by the prediction processing step, and performs an approach processing to reduce the disease risk for the user according to the classified risk group of the target disease.

[0009] Furthermore, one aspect of the present invention is a program that causes a computer to perform the following steps: a prediction processing step of predicting the disease risk of a target disease from a user's medical history information based on a trained model generated by performing machine learning using the past medical history information of multiple people as training data, which predicts the disease risk, which is the risk of developing and worsening of a target disease; and an intervention processing step of classifying the risk groups of the target disease based on the disease risk predicted by the prediction processing step, and performing an approach process to reduce the disease risk for the user according to the classified risk group of the target disease. [Effects of the Invention]

[0010] According to the present invention, the risk of a target disease can be accurately determined, and appropriate behavioral changes can be promoted. [Brief explanation of the drawing]

[0011] [Figure 1] This is a functional block diagram showing an example of a health support system according to this embodiment. [Figure 2] This figure shows an example of data in the resident information storage unit of this embodiment. [Figure 3] This figure shows an example of data in the risk result storage unit of this embodiment. [Figure 4] This figure shows an example of the input screen for the risk result extraction conditions in this embodiment. [Figure 5] This figure shows an example of a display screen for the extracted risk results in this embodiment. [Figure 6] This flowchart shows an example of the operation of the health support system according to this embodiment. [Figure 7] This figure shows an example of the operation of the health management application according to this embodiment. [Figure 8] This figure shows an example of the output information generation operation of the health support system according to this embodiment. [Modes for carrying out the invention]

[0012] Hereinafter, a health support system, a server device, a health support method, and a program according to an embodiment of the present invention will be described with reference to the drawings.

[0013] FIG. 1 is a functional block diagram showing an example of a health support system 1 according to the present embodiment. As shown in FIG. 1, the health support system 1 includes a health support server 10, an administrator terminal 20, and a user terminal 30.

[0014] The health support server 10, the administrator terminal 20, and the user terminal 30 can communicate with each other via a network NW1. Note that the health support system 1 can be connected to an external system 40 via the network NW1.

[0015] The external system 40 is various systems that can communicate with the health support system 1 via the network NW1, such as, for example, a national health insurance database (KDB) system, an electronic medical record of a hospital or a medical facility, a medical system such as a receipt (medical fee statement), and a corporate website that provides information on health appliances and health foods.

[0016] The health support server 10 (an example of a server device) predicts, for example, a disease risk that is the risk of onset and exacerbation of a target disease such as osteoporosis, extracts high-risk subjects for the target disease, and provides various information for prompting behavioral changes to reduce the disease risk to the high-risk subjects. The health support server 10 performs, for example, visualization of the disease risk of a target disease such as osteoporosis and extraction of high-risk subjects as a BI (Business Intelligence) tool. The health support server 10 further includes a NW (network) communication unit 11, a server storage unit 12, and a server control unit 13.

[0017] Note that the target diseases of the health support system 1 according to the present embodiment include osteoporosis, stroke, or dementia, but in the description of the present embodiment, mainly osteoporosis will be described as the target disease.

[0018] The NW communication unit 11 is, for example, a network adapter that can be connected to the network NW1 by a wired LAN or the like, and can be connected to the administrator terminal 20, the user terminal 30, and the external system 40 via the network NW1.

[0019] The server storage unit 12 is a storage unit realized by, for example, a RAM (Random Access Memory), an SSD (Solid State Drive), an HDD (Hard Disk Drive), etc., not shown, and stores various information used by the health support server 10. The server storage unit 12 includes a resident information storage unit 121, a model storage unit 122, a medical history information storage unit 123, a risk result storage unit 124, an inspection result storage unit 125, a recommendation information storage unit 126, and an output information storage unit 127.

[0020] The resident information storage unit 121 stores resident information about the residents of the local government who are users of the health support system 1. The resident information includes, for example, information such as the name, address, age, gender, telephone number, and email address of the resident (user). Here, referring to FIG. 2, the data example of the resident information storage unit 121 will be described.

[0021] FIG. 2 is a diagram showing a data example of the resident information storage unit 121 in the present embodiment. As shown in FIG. 2, the resident information storage unit 121 stores resident information in which the resident number, name, age, gender, address, telephone number, and email address are associated with each other.

[0022] Here, the resident number is an example of user identification information for identifying a resident (user), and a unique number is assigned to each resident (each user). For example, in the example shown in Figure 2, the resident with resident number "U00001" is identified as having the name "Taro XX", being 60 years old, and being male. Furthermore, the resident's address is "XXX-XX, XX Ward, Tokyo", their telephone number is "080-XXXX-XXXX", and their email address is "xxxx@abcd.com".

[0023] Returning to the explanation of Figure 1, the model memory unit 122 stores a trained model, which is a predictive model that predicts the disease risk of a target disease from the medical history information of the target residents. The trained model is, for example, a model generated by performing machine learning using the past medical history information of multiple people as training data. The medical history information includes, for example, medical record data, insurance claims, medical treatment data, health checkup results, and medication information, and the training data is supervised learning data that includes the diagnostic history of the target disease.

[0024] Furthermore, if the model storage unit 122 is responsible for multiple target diseases, it may store a trained model for each target disease. Furthermore, in this embodiment, the trained model is assumed to be created, for example, by a training processing server (not shown) and stored in the model storage unit 122 beforehand.

[0025] The medical history information storage unit 123 stores medical history information (e.g., medical record data, claims, medical treatment data, health checkup results, medication information, etc.) corresponding to residents (users) obtained from external systems 40 such as the KDB system or hospital and medical institution systems. The medical history information storage unit 123 stores, for example, resident numbers (user identification information) in association with medical history information.

[0026] The risk result storage unit 124 stores the risk determination results (risk results), which are the processing results of the treatment history determination unit 133, the prediction processing unit 134, and the intervention processing unit 135, which will be described later. The risk result storage unit 124 stores the risk results for each target resident. Now, with reference to Figure 3, an example of the data in the risk result storage unit 124 will be explained.

[0027] Figure 3 shows an example of data from the risk result storage unit 124 in this embodiment. As shown in Figure 3, the risk result storage unit 124 stores, for example, the resident number, the predicted risk value, the risk category, the treatment history, high risk, the test kit, and the app provision in association with each other.

[0028] Here, the risk prediction value is the predicted disease risk value predicted by the prediction processing unit 134, and the risk category is an example of a risk group classified by the intervention processing unit 135 based on the risk prediction value. Based on the risk prediction value, the risk is classified into less than 40%, 40% to less than 60%, 60% to less than 80%, and 80% or more. The treatment history is treatment history information determined by the treatment history determination unit 133, such as not treated, undergoing treatment, or treatment interrupted.

[0029] Furthermore, "high risk" indicates whether or not the individual is a high-risk individual with a disease risk above a certain level ("○" or "×"). "Test kit" indicates whether or not a test kit, such as for osteoporosis, was sent. "App provision" indicates whether or not the health management application described later was provided.

[0030] In the example shown in Figure 3, the resident (user) with resident number "U00001" has a predicted risk value of "20.7%", a risk category of "less than 40%", and a treatment history of "untreated". Furthermore, this resident (user) is marked as "×" for high risk, test kit availability, and app provision.

[0031] Furthermore, the resident (user) with resident number "U00002" has a risk prediction value of "85.5%", a risk category of "80% or higher", and a treatment history of "treatment interrupted". In addition, this resident (user) has indicated "○" for high risk, test kit, and app provision.

[0032] Returning to the explanation of Figure 1, the test result storage unit 125 stores the test results obtained using the test kit described above. The test result storage unit 125 stores, for example, the resident number and the test result in association.

[0033] The recommendation information storage unit 126 stores recommendation information (recommendation information) for health devices and health foods that reduce disease risk, obtained from an external system 40, such as a server device of a cooperating company. The recommendation information storage unit 126 may store recommendation information (recommendation information) for each risk category and for each target disease.

[0034] The output information storage unit 127 stores the output information generated by the output processing unit 137, which will be described later. The output information is, for example, display information such as a list of risk results extracted from the risk results in response to a request (extraction conditions) from the administrator terminal 20.

[0035] The server control unit 13 is a functional unit that is realized, for example, by having a CPU (Central Processing Unit) (not shown) execute a program stored in the server memory unit 52, and comprehensively controls the health support server 10. The server control unit 13 executes various processes performed by the health support server 10. The server control unit 13 includes a medical history information acquisition unit 131, a resident extraction unit 132, a treatment history determination unit 133, a prediction processing unit 134, an intervention processing unit 135, an AP processing unit 136, and an output processing unit 137.

[0036] The medical history information acquisition unit 131 acquires medical history information from the external system 40. The medical history information acquisition unit 131 acquires medical history information (for example, medical record data, claims, medical treatment data, health checkup results, medication information, etc.) from the external system 40 via the NW communication unit 11, associates the acquired medical history information with the resident number, and stores it in the medical history information storage unit 123.

[0037] The resident extraction unit 132 extracts, for example, residents who are subject to risk assessment from the resident information storage unit 121.

[0038] The treatment history determination unit 133 determines the treatment status of the target disease (e.g., osteoporosis) from the medical history information. For example, the treatment history determination unit 133 determines whether the treatment history is untreated, in treatment, or discontinued, based on the medical history information and medication information related to the target disease.

[0039] The prediction processing unit 134 predicts the disease risk of a target disease from the medical history information of the target resident (user) based on the trained model described above. The prediction processing unit 134 obtains the medical history information corresponding to the target resident (user) from the medical history information storage unit 123, and uses the trained model stored in the model storage unit 122 to calculate a prediction value, such as the risk prediction value shown in Figure 3, from the obtained medical history information.

[0040] The intervention processing unit 135 classifies the risk groups of the target disease based on the disease risk predicted by the prediction processing unit 134, and executes an approach process to reduce the disease risk for the user according to the classified risk group of the target disease. For example, the intervention processing unit 135 classifies the risk groups of the target disease, such as risk categories, based on the risk prediction values ​​shown in Figure 3.

[0041] Furthermore, the intervention processing unit 135 extracts users in the high-risk group (high-risk subjects) whose disease risk is above a certain level, and executes an approach process to encourage behavioral changes that reduce disease risk for users in the high-risk group. For example, the intervention processing unit 135 extracts target residents whose risk classification as shown in Figure 3 is "80% or higher" as high-risk subjects.

[0042] The intervention processing unit 135 sends an outcome notification indicating that the high-risk individual is a high-risk individual (high-risk group) or a notification encouraging behavioral change (for example, a notification such as "exercise regularly") as an approach process to the high-risk individual. The intervention processing unit 135 obtains the telephone number or email address corresponding to the high-risk individual from the resident information storage unit 121 and sends the notification to the telephone number or email address of the high-risk individual. The intervention processing unit 135 may also send the outcome notification or the notification encouraging behavioral change to the high-risk individual by mail.

[0043] Furthermore, the intervention processing unit 135 sends a test kit for the target disease to high-risk individuals (users in the high-risk group) as an approach process. For example, the intervention processing unit 135 arranges for an osteoporosis test kit to be mailed to the address of a high-risk individual obtained from the resident information storage unit 121.

[0044] Furthermore, the intervention processing unit 135 provides high-risk individuals with a health management application to support their health management as an approach. The intervention processing unit 135 provides high-risk individuals with the health management application by notifying them, for example, of a URL (Uniform Resource Locator) to which the health management application can be downloaded, via mail or email.

[0045] The intervention processing unit 135 stores the disease risk assessment result (risk result) of the target residents in the risk result storage unit 124. The intervention processing unit 135 stores the risk result in the risk result storage unit 124, for example, as shown in Figure 3.

[0046] Furthermore, the intervention processing unit 135 may send different notifications to the target residents depending on the risk classification (risk group). Furthermore, the intervention processing unit 135 may classify individuals into risk categories (risk groups) or extract high-risk individuals based on the risk prediction value and treatment history of the target disease. For example, even if the risk prediction value is high, if the treatment history is "under treatment," the individual may be classified into a low-risk category (risk group) for the disease risk. Alternatively, if the treatment history is "untreated," the individual may be extracted as a high-risk individual even if the low-risk category (risk group) is "60% or more but less than 80%."

[0047] The AP processing unit 136 (an example of an application processing unit) provides health management support services through a health management application. The AP processing unit 136, via the NW communication unit 11, performs tasks such as visualizing risk results and providing recommendation information (suggested information) for health equipment and supplements to user terminals 30 connected to the health support server 10.

[0048] The AP processing unit 136 outputs output information (e.g., risk results) to high-risk individuals (user terminals 30), including disease risk, medical history information, and risk groups for the target disease. The AP processing unit 136 generates output information based on the risk results stored in the risk result storage unit 124, and transmits the generated output information to the user terminal 30 via the NW communication unit 11, causing the user terminal 30 to display the output information.

[0049] Furthermore, the AP processing unit 136 provides, for example, information on health devices or health foods to reduce disease risk to high-risk individuals (user terminals 30). The AP processing unit 136 collects information on health devices or health foods from an external system 40 via the NW communication unit 11 and stores it in the recommendation information storage unit 126. The AP processing unit 136 acquires recommendation information according to the risk classification (risk group) of the target residents (users) and transmits it to the user terminal 30 via the NW communication unit 11 as information on health devices or health foods, causing the user terminal 30 to display the recommendation information.

[0050] The output processing unit 137 generates output information, for example, in response to a request from the administrator terminal 20, which includes disease risk for each target resident (user), risk group for the target disease, and treatment history for the target disease. The output processing unit 137 transmits the generated output information to the administrator terminal 20 via the NW communication unit 11, causing the administrator terminal 20 to display the output information (display information).

[0051] Now, with reference to Figures 4 and 5, the output information output by the output processing unit 137 will be explained. Figure 4 shows an example of the input screen for the risk result extraction conditions in this embodiment. Figure 5 shows an example of a display screen for the extracted risk results in this embodiment.

[0052] The output processing unit 137 displays, for example, the display screen G1 shown in Figure 4 on the administrator terminal 20, and obtains the risk result extraction conditions from the administrator terminal 20 via the NW communication unit 11.

[0053] Furthermore, the output processing unit 137 extracts risk results from the risk result storage unit 124 according to the risk result extraction conditions and generates output information such as the display screen G2 shown in Figure 5. The output processing unit 137 transmits the output information (display screen G2) to the administrator terminal 20 via the NW communication unit 11, causing the administrator terminal 20 to display the output information (display screen G2).

[0054] Returning to the explanation of Figure 1, the administrator terminal 20 is a terminal device owned by the administrator of the health support system 1, and is, for example, a personal computer (PC), a tablet terminal, or a smartphone. The administrator terminal 20 comprises an NW communication unit 21, an input unit 22, a display unit 23, a terminal control unit 24, and a terminal storage unit 25.

[0055] The NW communication unit 21 is a network adapter that can connect to the network NW1 via, for example, a wired LAN or wireless LAN, and can connect to the health support server 10 via the network NW1.

[0056] The input unit 22 is, for example, an input device such as a keyboard, mouse, or touch panel, and receives various information from the administrator.

[0057] The display unit 23 is, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display. The display unit 23 displays various information for managing the health support system 1.

[0058] The terminal control unit 24 is a functional unit that is implemented, for example, by having a CPU (not shown) execute a program stored in the terminal memory unit 25, and comprehensively controls the administrator terminal 20. The terminal control unit 24 performs various processes for managing the health support system 1.

[0059] The terminal storage unit 25 is a storage unit implemented by, for example, RAM, SSD, HDD, etc. (not shown), and stores various information used by the administrator terminal 20.

[0060] The user terminal 30 is a terminal device owned by a user (target resident) of the health support system 1, and is, for example, a personal computer (PC), tablet terminal, or smartphone. The user terminal 30 comprises an NW communication unit 31, an input unit 32, a display unit 33, a terminal control unit 34, and a terminal storage unit 35.

[0061] The NW communication unit 31 is a network adapter that can connect to the network NW1 via, for example, a wired LAN or wireless LAN, and can connect to the health support server 10 via the network NW1.

[0062] The input unit 32 is, for example, an input device such as a keyboard, mouse, or touch panel, and receives various information from the administrator.

[0063] The display unit 33 is, for example, a display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display. The display unit 33 displays various information when using the health management application described above.

[0064] The terminal control unit 34 is a functional unit that is implemented, for example, by having a CPU (not shown) execute a program stored in the terminal memory unit 35, and comprehensively controls the user terminal 30. The terminal control unit 34 performs various processes when using the health management application described above, for example.

[0065] The terminal storage unit 35 is a storage unit implemented by, for example, RAM, SSD, HDD, etc. (not shown), and stores various information used by the user terminal 30. For example, the health management application program provided by the health support system 1 is installed and stored in the terminal storage unit 35.

[0066] In Figure 1, the health support system 1 is shown to have one user terminal 30, but the health support system 1 may be equipped with multiple user terminals 30 depending on the user.

[0067] Next, the operation of the health support system 1 according to this embodiment will be described with reference to the drawings. Figure 6 is a flowchart showing an example of the operation of the health support system 1 according to this embodiment.

[0068] In the health support system 1, for example, in response to a request from the administrator terminal 20, or when predetermined execution conditions (for example, every certain period of time) are met, the health support server 10 starts assessing the risk of a target disease (for example, osteoporosis).

[0069] As shown in Figure 6, the health support server 10 of the health support system 1 first acquires medical history information (step S101). The medical history information acquisition unit 131 of the health support server 10 acquires medical history information (e.g., medical record data, claims, medical treatment data, health checkup results, medication information, etc.) from the external system 40 via the network communication unit 11. The medical history information acquisition unit 131 associates the acquired medical history information with the resident number and stores it in the medical history information storage unit 123.

[0070] Next, the resident extraction unit 132 of the health support server 10 extracts residents from the resident information storage unit 121 who are subject to disease risk assessment (step S102). The resident extraction unit 132 may, for example, extract residents in response to a request from the administrator terminal 20, or it may extract residents who meet predetermined conditions.

[0071] Next, the treatment history determination unit 133 of the health support server 10 determines the treatment history from the medical history information corresponding to the target resident (step S103). The treatment history determination unit 133 obtains the medical history information corresponding to the target resident from the medical history information storage unit 123 and determines the treatment history (one of not being treated, undergoing treatment, or treatment being interrupted) from, for example, medical information and medication information (medication information) from the medical claim form.

[0072] Next, the prediction processing unit 134 of the health support server 10 predicts disease risk from medical history information corresponding to the target residents using a trained model (step S104). The prediction processing unit 134 obtains medical history information corresponding to the target residents from the medical history information storage unit 123, and uses the trained model stored in the model storage unit 122 to calculate a prediction value, such as the risk prediction value shown in Figure 3, from the obtained medical history information.

[0073] Next, the intervention processing unit 135 of the health support server 10 classifies the disease risk categories of the target residents (for example, the risk categories shown in Figure 3) based on the predicted disease risk values ​​(step S105).

[0074] Next, the intervention processing unit 135 extracts high-risk individuals from the target population based on risk classification and treatment history (step S106). For example, the intervention processing unit 135 extracts high-risk individuals by considering treatment history (untreated, undergoing treatment, or treatment discontinued) in addition to the risk classification.

[0075] Next, the intervention processing unit 135 notifies high-risk individuals of the disease risk assessment results and sends them disease testing kits (step S107). The intervention processing unit 135 obtains the high-risk individuals' telephone numbers or email addresses from the resident information storage unit 121 and uses these telephone numbers or email addresses to notify the high-risk individuals of the disease risk assessment results. The intervention processing unit 135 also obtains the high-risk individuals' addresses from the resident information storage unit 121 and arranges for, for example, an osteoporosis testing kit to be mailed to the high-risk individuals' addresses.

[0076] Next, the intervention processing unit 135 provides a health management application to high-risk individuals (step S108). The intervention processing unit 135 provides the health management application to high-risk individuals by, for example, mailing or emailing them a URL where they can download the health management application.

[0077] Next, the intervention processing unit 135 acquires the test results from the test kit (step S109). The intervention processing unit 135 acquires, for example, the test results from an osteoporosis test kit from an external system 40 via the NW communication unit 11. The intervention processing unit 135 stores the risk results, for example, as shown in Figure 3, in the risk result storage unit 124.

[0078] Furthermore, the intervention processing unit 135 may, after obtaining the test results from the test kit, reconsider the test results and reassess the disease risk. After the processing in step S109, the intervention processing unit 135 terminates the process.

[0079] Next, with reference to Figure 7, the operation of the health management application according to this embodiment will be described. Figure 7 shows an example of the operation of the health management application according to this embodiment.

[0080] As shown in Figure 7, first, the user terminal 30 performs an access process for the health support application to the health support server 10 (step S201). The terminal control unit 34 of the user terminal 30 executes the health management application installed in the terminal storage unit 35 and starts access processing with the health support server 10 via the NW communication unit 31.

[0081] Next, the user terminal 30 sends a request to the health support server 10 for output of the disease risk assessment result (step S202). The terminal control unit 34, for example, in response to receiving input information from the input unit 32, sends a request to the health support server 10 for output of the disease risk assessment result via the network communication unit 31.

[0082] Next, the health support server 10 sends the disease risk determination result to the user terminal 30 in response to a request for output of the disease risk determination result (step S203). The AP processing unit 136 of the health support server 10 obtains the risk result (disease risk determination result) for the target resident corresponding to the user terminal 30 from the risk result storage unit 124, and sends the obtained risk result (disease risk determination result) for the target resident to the user terminal 30 via the NW communication unit 11.

[0083] Next, the user terminal 30 displays the risk assessment result on the display unit 33 (step S204). The terminal control unit 34 displays the risk result (disease risk assessment result) received from the health support server 10 via the NW communication unit 31 on the display unit 33, making the risk result visible.

[0084] Next, the health support server 10 determines whether the user's risk is high or not (step S205). The AP processing unit 136 determines whether the user (target resident) is high-risk (a high-risk individual) based on whether the high-risk item included in the user's risk result is marked with "○". If the user (target resident) is high-risk (a high-risk individual) (step S205: YES), the AP processing unit 136 proceeds to step s206. If the user (target resident) is not high-risk (a high-risk individual) (step S205: NO), the AP processing unit 136 proceeds to step s208.

[0085] In step S206, the AP processing unit 136 sends a treatment alert notification (step S2-6). The AP processing unit 136 sends, for example, a warning message urging the user to receive treatment as a treatment alert to the user terminal 30 via the NW communication unit 11.

[0086] Next, the terminal control unit 34 of the user terminal 30 displays the treatment alert on the display unit 33 (step S207). The terminal control unit 34 displays the treatment alert notification (e.g., a warning message) received from the health support server 10 via the NW communication unit 31 on the display unit 33.

[0087] Furthermore, in step S208, the AP processing unit 136 extracts recommendation information according to disease risk. The AP processing unit 136 extracts recommendation information (recommended information) according to the risk category (risk group) of the target residents (users), such as information on health equipment or health foods (supplements, etc.).

[0088] Next, the AP processing unit 136 transmits the recommendation information to the user terminal 30 (step S209). The AP processing unit 136 transmits the recommendation information to the user terminal 30 via the NW communication unit 11.

[0089] Next, the user terminal 30 displays the recommendation information on the display unit 33 (step S210). The terminal control unit 34 displays the recommendation information for health devices, supplements, etc., received from the health support server 10 via the NW communication unit 31 on the display unit 33.

[0090] Next, with reference to Figure 8, the output information generation operation of the health support system 1 according to this embodiment will be described. Figure 8 shows an example of the output information generation operation of the health support system 1 according to this embodiment.

[0091] As shown in Figure 8, first, the administrator terminal 20 performs a login process to the health support server 10 (step S301). The terminal control unit 24 of the administrator terminal 20 performs a login process with the health support server 10 via the NW communication unit 21.

[0092] Next, the health support server 10 sends the management menu information to the administrator terminal 20 (step S302). The output processing unit 137 of the health support server 10 sends the management menu information to the administrator terminal 20 via the NW communication unit 11.

[0093] Next, the administrator terminal 20 displays the management menu on the display unit 23 (step S303). The terminal control unit 24 of the administrator terminal 20 displays the management menu on the display unit 23 based on the management menu information received from the health support server 10 via the NW communication unit 21.

[0094] Next, the terminal control unit 24 receives menu selection information and extraction conditions from the input unit 22 (step S304).

[0095] Next, the terminal control unit 24 sends an extraction request (menu selection information and extraction conditions) to the health support server 10 (step S305). The terminal control unit 24 sends the extraction request, including the menu selection information and extraction conditions received from the input unit 22, to the health support server 10 via the network communication unit 21.

[0096] Next, the output processing unit 137 of the health support server 10 extracts risk assessment results that match the extraction conditions from the risk assessment results in response to the extraction request and generates output information (step S306). The output processing unit 137 generates output information such as the display screen G2 shown in Figure 5.

[0097] Next, the output processing unit 137 transmits the output information to the administrator terminal 20 (step S307). The output processing unit 137 transmits the output information to the administrator terminal 20 via the NW communication unit 11.

[0098] Next, the terminal control unit 24 displays the output information on the display unit 23 (step S308). The terminal control unit 24 displays the output information received from the health support server 10 (for example, the display screen G2 shown in Figure 5) on the display unit 23 via the NW communication unit 21.

[0099] As described above, the health support system 1 according to this embodiment comprises a prediction processing unit 134 and an intervention processing unit 135. The prediction processing unit 134 predicts the disease risk of a target disease (e.g., osteoporosis) from the medical history information of the user (target resident) based on a trained model generated by performing machine learning using past medical history information of multiple people (e.g., medical record data, claims, medical treatment data, health checkup results, medication information, etc.) as training data, which is the risk of disease risk, and which is the risk of developing and worsening of the target disease. The intervention processing unit 135 classifies the risk group (e.g., risk category) of the target disease based on the disease risk predicted by the prediction processing unit 134, and executes an approach process to reduce the disease risk for the user (target resident) according to the classified risk group (e.g., risk category) of the target disease.

[0100] As a result, the health support system 1 according to this embodiment predicts disease risk based on a trained model generated by machine learning using past medical history information of multiple individuals (e.g., medical record data, claims, medical treatment data, health checkup results, medication information, etc.) as training data, thereby enabling accurate determination of the risk of the target disease. Furthermore, the health support system 1 according to this embodiment executes an approach process to reduce disease risk according to the risk group (e.g., risk category) based on the disease risk, thereby enabling the target residents (users) to become aware of their disease risk and encourage appropriate behavioral changes. Therefore, the health support system 1 according to this embodiment can accurately determine the risk of the target disease and encourage appropriate behavioral changes, such as improving lifestyle habits or visiting medical institutions.

[0101] Furthermore, in this embodiment, the intervention processing unit 135 extracts users in the high-risk group (high-risk subjects) whose disease risk is above a certain level, and executes an approach process to encourage behavioral changes that reduce disease risk for users in the high-risk group.

[0102] As a result, the health support system 1 according to this embodiment executes an approach process that encourages behavioral changes to reduce disease risk for users in the high-risk group, thereby making the target residents (users) aware of their disease risk and further encouraging appropriate behavioral changes.

[0103] Furthermore, in this embodiment, the intervention processing unit 135 sends a result notification indicating that the user belongs to the high-risk group (high-risk target person) or a notification encouraging behavioral change as an approach process.

[0104] As a result, the health support system 1 according to this embodiment can make the target residents (users) aware that they belong to a high-risk group by sending a result notification indicating that they belong to a high-risk group or a notification encouraging behavioral change, and can further appropriately encourage behavioral change.

[0105] Furthermore, in this embodiment, the intervention processing unit 135 sends (for example, by mail) a test kit for the target disease to users in the high-risk group (high-risk subjects) as an approach process.

[0106] As a result, the health support system 1 according to this embodiment can make target residents (users) aware of their disease risk by sending (for example, by mail) a test kit for the target disease, and further encourage appropriate behavioral changes.

[0107] Furthermore, in this embodiment, the intervention processing unit 135 provides a health management application to support the health management of users in the high-risk group (high-risk subjects) as an approach process.

[0108] As a result, the health support system 1 according to this embodiment can make target residents (users) aware of their disease risks by providing a health management application, and further encourage appropriate behavioral changes.

[0109] Furthermore, the health support system 1 according to this embodiment includes an AP processing unit 136 (application processing unit) that provides health management support services via a health management application. The AP processing unit 136 provides users in the high-risk group (high-risk individuals) with information on health devices or health foods that reduce the risk of disease.

[0110] As a result, the health support system 1 according to this embodiment provides information on health devices or health foods for reducing disease risk through a health management application, thereby raising the health management awareness of the target residents (users) and reducing disease risk.

[0111] Furthermore, in this embodiment, the AP processing unit 136 outputs output information (e.g., risk results) to users in the high-risk group (high-risk subjects), including disease risk, medical history information, and the risk group of the target disease.

[0112] As a result, the health support system 1 according to this embodiment can output output information (e.g., risk results) that includes disease risk, medical history information, and risk groups for the target disease, thereby making the target residents (users) aware of their disease risk and further encouraging appropriate behavioral changes.

[0113] Furthermore, in this embodiment, the target diseases include osteoporosis, stroke, or dementia. As a result, the health support system 1 according to this embodiment can make target residents (users) aware of the disease risk for osteoporosis, stroke, and dementia, which are diseases that progress without subjective symptoms, and can appropriately encourage behavioral change.

[0114] Furthermore, the health support server 10 (server device) according to this embodiment comprises a prediction processing unit 134 and an intervention processing unit 135. The prediction processing unit 134 predicts the disease risk of a target disease (e.g., osteoporosis) from the medical history information of the user (target resident), based on a trained model generated by machine learning using the past medical history information of multiple people as training data, which predicts the disease risk, which is the risk of developing and worsening of a target disease (e.g., osteoporosis). The intervention processing unit 135 classifies the risk group (e.g., risk category) of the target disease based on the disease risk predicted by the prediction processing unit 134, and executes an approach process to reduce the disease risk for the user according to the classified risk group (e.g., risk category) of the target disease.

[0115] As a result, the health support server 10 (server device) according to this embodiment can achieve the same effects as the health support system 1 described above, accurately determine the risk of target diseases, and encourage appropriate behavioral changes.

[0116] Furthermore, the health support method according to this embodiment includes a prediction processing step and an intervention processing step. In the prediction processing step, the prediction processing unit 134 predicts the disease risk of a target disease from the user's medical history information based on a trained model generated by machine learning using the past medical history information of multiple people as training data, which predicts disease risk, which is the risk of developing and worsening of the target disease. In the intervention processing step, the intervention processing unit 135 classifies the risk groups of the target disease based on the disease risk predicted in the prediction processing step, and executes an approach process to reduce the disease risk for the user according to the classified risk group of the target disease.

[0117] As a result, the health support method according to this embodiment can achieve the same effects as the health support system 1 and health support server 10 described above, accurately determining the risk of target diseases and promoting appropriate behavioral changes.

[0118] It should be noted that the present invention is not limited to the embodiments described above, and can be modified without departing from the spirit of the invention. For example, in the above embodiment, the target diseases were described as osteoporosis, stroke, and dementia, but the invention is not limited to these. Any disease that progresses without subjective symptoms may be used.

[0119] Furthermore, although the above embodiment describes an example in which the health support server 10 is implemented by a single server device, it is not limited to this and may be implemented by multiple server devices. For example, the AP processing unit 136 may be configured to be included in the application server. Also, for example, part or all of the server storage unit 12 may be included in the storage server.

[0120] Furthermore, in the above embodiment, an example was described in which the administrator terminal 20 displays the output information generated by the output processing unit 137 (for example, a list of risk results). However, the system is not limited to this, and for example, output information (for example, a list of risk results) that has been determined for the risk of residents may be provided to the local government. In this case, the health support system 1 may, for example, determine the disease risk of residents on behalf of the local government, extract high-risk individuals, provide a list of high-risk individuals, and perform various approaches to encourage behavioral change in exchange for a fee.

[0121] Furthermore, in the above embodiment, an example of a risk group was described in which a risk classification is used in which the predicted value of disease risk is divided into a predetermined range. However, the system is not limited to this, and the health support system 1 may use other risk groups, such as risk levels or risk ranks that take into account other items such as treatment history in addition to the predicted value of disease risk.

[0122] Furthermore, each component of the health support system 1 and health support server 10 described above has a computer system inside. The processing in each component of the health support system 1 and health support server 10 may be performed by recording a program for realizing the functions of each component on a computer-readable recording medium, loading the program recorded on this recording medium into the computer system, and executing it. Here, "loading the program recorded on the recording medium into the computer system and executing it" includes installing the program into the computer system. Here, "computer system" includes hardware such as the operating system and peripheral devices. Furthermore, "computer system" may include multiple computer devices connected via a network, including communication lines such as the Internet, WAN, LAN, and dedicated lines. "Computer-readable recording medium" refers to portable media such as flexible disks, magneto-optical disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into computer systems. Thus, the recording medium storing the program may be a non-transient recording medium such as a CD-ROM.

[0123] Furthermore, the recording medium also includes internal or external recording media accessible from the distribution server for distributing the program. The program may be divided into multiple parts, downloaded at different times, and then combined in the respective configurations of the health support system 1 and health support server 10. The distribution servers for each of the divided programs may also be different. Moreover, "computer-readable recording media" includes volatile memory (RAM) within computer systems that act as servers or clients when a program is transmitted over a network, which retains the program for a certain period of time. The program itself may also be intended to implement some of the functions described above. Furthermore, the program may be a so-called differential file (differential program) that can implement the functions described above in combination with a program already recorded in the computer system.

[0124] Furthermore, some or all of the above-mentioned functions may be implemented as integrated circuits such as LSIs (Large Scale Integrations). Each of the above-mentioned functions may be implemented as an individual processor, or some or all of them may be integrated into a single processor. In addition, the method of implementing integrated circuits is not limited to LSIs; they may also be implemented using dedicated circuits or general-purpose processors. Furthermore, if advances in semiconductor technology lead to the emergence of integrated circuit technologies that can replace LSIs, integrated circuits using such technologies may be used. [Explanation of symbols]

[0125] 1. Health support system 10 Health support server 11, 21, 31 NW Communications Department 12 Server Storage 13 Server Control Unit 20 Administrator terminals 22, 32 Input section 23, 33 Display section 24, 34 Terminal Control Unit 25, 35 Terminal storage unit 40 External Systems 121 Resident information storage section 122 Model Memory Unit 123 Medical History Information Storage Unit 124 Risk Outcome Memory Unit 125 Test Result Storage Unit 126 Recommended Information Storage Unit 127 Output Information Storage Unit 131 Medical History Information Acquisition Department 132 Resident Selection Department 133 Treatment History Determination Unit 134 Prediction Processing Unit 135 Intervention Processing Section 136 AP Processing Unit 137 Output Processing Unit

Claims

1. A pre-trained model generated by performing machine learning using the past medical history information of multiple individuals as training data, which predicts disease risk, which is the risk of developing and worsening of a target disease, is used as the basis for a prediction processing unit that predicts the disease risk of the target disease from the user's medical history information. Based on the disease risk predicted by the prediction processing unit, the intervention processing unit classifies the risk groups of the target disease, and, according to the classified risk group of the target disease, executes an approach process to reduce the disease risk for the user. A health support system equipped with these features.

2. The intervention processing unit extracts users in the high-risk group whose disease risk is above a certain level, and executes the approach processing for the users in the high-risk group to encourage behavioral changes that reduce the disease risk. The health support system according to claim 1.

3. The intervention processing unit transmits to the high-risk group users, as an approach process, a result notification indicating that they belong to the high-risk group or a notification encouraging behavioral change. The health support system according to claim 2.

4. The intervention processing unit transmits the target disease testing kit to the high-risk group of users as part of the approach processing. The health support system according to claim 2.

5. The intervention processing unit provides the high-risk group of users with a health management application to support their health management as an approach process. The health support system according to claim 2.

6. The system includes an application processing unit that provides support services for health management via the aforementioned health management application, The application processing unit provides the high-risk group of users with information regarding health devices or health foods that reduce the risk of the disease. The health support system according to claim 5.

7. The application processing unit outputs output information to the high-risk group of users, including the disease risk, medical history information, and the risk group of the target disease. The health support system according to claim 6.

8. The intervention processing unit classifies the risk group of the target disease based on the disease risk and the treatment history of the target disease. A health support system according to any one of claims 1 to 7.

9. The system includes an output processing unit that generates output information including the disease risk for each user, the risk group for the target disease, and the treatment history for the target disease. The health support system according to claim 8.

10. The aforementioned target diseases include osteoporosis, stroke, or dementia. A health support system according to any one of claims 1 to 7.

11. A pre-trained model generated by performing machine learning using the past medical history information of multiple individuals as training data, which predicts disease risk, which is the risk of developing and worsening of a target disease, is used as the basis for a prediction processing unit that predicts the disease risk of the target disease from the user's medical history information. Based on the disease risk predicted by the prediction processing unit, the intervention processing unit classifies the risk groups of the target disease, and, according to the classified risk group of the target disease, executes an approach process to reduce the disease risk for the user. A server device equipped with the following features.

12. A prediction processing step involves a prediction processing unit that uses the past medical history information of multiple individuals as training data to perform machine learning and generate a trained model that predicts disease risk, which is the risk of developing and worsening of a target disease, and then predicts the disease risk of the target disease from the user's medical history information. An intervention processing step in which the intervention processing unit classifies the risk group of the target disease based on the disease risk predicted by the prediction processing step, and executes an approach process to reduce the disease risk for the user according to the classified risk group of the target disease. Health support methods including

13. On the computer, A pre-trained model generated by performing machine learning using the past medical history information of multiple individuals as training data, which predicts disease risk, which is the risk of developing and worsening of a target disease, is used as the basis for a prediction processing step that predicts the disease risk of the target disease from the user's medical history information. Based on the disease risk predicted by the prediction processing step, an intervention processing step is performed to classify the risk groups of the target disease, and to perform an approach processing to reduce the disease risk for the user according to the classified risk group of the target disease. A program to execute.

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