Health management assistance device, health management assistance method, program, and recording medium

Abstract

The purpose of the present disclosure is to provide a health management assistance device for assisting health management on the basis of individualized health indexes.　This health management assistance device includes an information acquisition unit, a disease onset prediction unit, a health index calculation unit, and an information output unit. The information acquisition unit acquires disease-related information about prediction subjects, the disease onset prediction unit predicts disease onset risks for the prediction subjects on the basis of the disease-related information, the health index calculation unit calculates respective health indexes for the prediction subjects on the basis of the disease onset risks, and the information output unit outputs disease onset prediction information on the basis of the health indexes.

Description

Health management support device, health management support method, program, and recording medium

[0001] The present disclosure relates to a health management support device, a health management support method, a program, and a recording medium.

[0002] The need for preventive medicine is increasing, and individuals are being asked to take the initiative in managing their health. Patent Literature 1 discloses a prediction device for predicting the prognosis of a patient with acute myocardial infarction, the prediction device including an acquisition unit, a prediction unit, and an output unit, the acquisition unit acquiring at least one feature of the patient, the prediction unit predicting the prognosis based on the at least one feature using a trained prediction model, and the output unit outputting the predicted prognosis.

[0003] Patent No. 2022-184475

[0004] The disease development risk prediction device of Patent Document 1 predicts an individual's disease development risk based on a prediction model created using sample data of multiple patients as training data. However, a prediction model created using sample data of multiple patients as training data, such as that of Patent Document 1, i.e., a disease development risk index calculated based on statistical data, does not necessarily apply to an individual.

[0005] Therefore, an object of the present disclosure is to provide a health management support device, a health management support method, a program, and a recording medium for supporting health management based on individualized health indices.

[0006] In order to achieve the above-mentioned objective, the health management support device of the present disclosure includes an information acquisition unit, a disease onset prediction unit, a health index calculation unit, and an information output unit, wherein the information acquisition unit acquires disease-related information of the predicted subject, the disease onset prediction unit predicts the disease onset risk of the predicted subject based on the disease-related information, the health index calculation unit calculates a health index for each predicted subject based on the disease onset risk, and the information output unit outputs disease onset prediction information based on the health index.

[0007] The health management support method disclosed herein includes an information acquisition process, a disease onset prediction process, a health index calculation process, and an information output process, wherein the information acquisition process acquires disease-related information for a predicted subject, the disease onset prediction process predicts the disease onset risk of the predicted subject based on the disease-related information, the health index calculation process calculates a health index for each predicted subject based on the disease onset risk, and the information output process outputs disease onset prediction information based on the health index.

[0008] The program of the present disclosure is a program for causing a computer to execute the steps of the method of the present disclosure as procedures.

[0009] The recording medium of the present disclosure is a computer-readable recording medium on which the program of the present disclosure is recorded.

[0010] According to the present disclosure, health management can be supported based on individualized health indicators.

[0011] FIG. 1 is a block diagram showing an example of the configuration of a health management support device of the present disclosure. FIG. 2 is a block diagram showing an example of the hardware configuration of a health management support device of the present disclosure. FIG. 3 is a flowchart showing an example of processing in the health management support device of the present disclosure. FIG. 4 is an example of a case where the disease-related information includes disease-related information acquired in real time. FIG. 5 is an example of a case where the disease onset risk is a disease onset risk collected at regular intervals. FIG. 6 is an example of extracting data related to the disease (heart rate) of the prediction target from disease-related information acquired in real time, and predicting the disease onset risk of the prediction target person based on the extracted data by regression analysis. FIG. 7 is an example of a calculated health index. FIG. 8 is a diagram showing an example of output statistical information.

[0012] Embodiments of the present disclosure will be described with reference to the drawings. The present disclosure is not limited to the following embodiments. In the following drawings, the same parts are denoted by the same reference numerals. Furthermore, the descriptions of the embodiments can be mutually incorporated unless otherwise specified, and the configurations of the embodiments can be combined unless otherwise specified.

[0013] 1 is a block diagram showing an example of the configuration of a health management support device 10 (hereinafter also referred to as "the device 10") according to the present disclosure. As shown in FIG. 1, the device 10 includes an information acquisition unit 11, a disease onset prediction unit 12, a health index calculation unit 13, and an information output unit 14.

[0014] The device 10 may be, for example, a single device including the above-mentioned components, or a device in which the components can be connected via a communication network. The device 10 may also be connected to an external device (described later) via the communication network. The communication network is not particularly limited and may be any known network, for example, wired or wireless. Examples of the communication network include the Internet, the World Wide Web (WWW), a telephone line, a Local Area Network (LAN), a Storage Area Network (SAN), a Delay Tolerant Networking (DTN), a Low Power Wide Area Network (LPWA), and a Local 5G (L5G). Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), local 5G, and LPWA. The wireless communication may be a form in which each device communicates directly (ad hoc communication), infrastructure communication, indirect communication via an access point, or the like. The device 10 may be incorporated into a system server, for example. The device 10 may also be, for example, a personal computer (PC, e.g., desktop or laptop) on which the program of the present disclosure is installed, a smartphone, a tablet terminal, or the like. Furthermore, the device 10 may be, for example, in the form of cloud computing or edge computing, in which at least one of the components is located on a server and the other components are located on a terminal.

[0015] 2 is a block diagram illustrating an example of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, an output device 106, and a communication device 107. The components of the device 10 are connected to each other via the bus 103 and their respective interfaces (I / F).

[0016] The central processing unit 101 cooperates with other components via a controller (such as a system controller or an I / O controller) and controls the entire device 10. In the device 10, the central processing unit 101 executes, for example, the program disclosed herein and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 101 functions as an information acquisition unit 11, a disease onset prediction unit 12, a health index calculation unit 13, and an information output unit 14. The device 10 may include other computing devices such as a CPU, a GPU (Graphics Processing Unit), or an APU (Accelerated Processing Unit) as a computing device, or may include a CPU in combination with these.

[0017] The bus 103 can also be connected to, for example, external devices. Examples of the external devices include a user terminal, an external storage device (such as an external database), a printer, an external input device, an external display device, and an external imaging device. The device 10 can be connected to an external network (the communication line network) by, for example, a communication device 107 connected to the bus 103, and can also be connected to other devices via the external network.

[0018] The memory 102 may be, for example, a main memory (primary storage device). When the central processing unit 101 performs processing, the memory 102 reads various operating programs, such as the program of the present disclosure, stored in the storage device 104 (described later), and the central processing unit 101 receives data from the memory 102 and executes the programs. The main memory may be, for example, a RAM (random access memory). Alternatively, the memory 102 may be, for example, a ROM (read only memory).

[0019] The storage device 104 is also referred to as an auxiliary storage device, for example, in contrast to the main memory (primary storage device). As described above, the storage device 104 stores an operating program including the program of the present disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive that reads and writes from and to the recording medium. The recording medium is not particularly limited and may be, for example, an internal or external type, and examples include a hard disk (HD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, and memory card. The storage device 104 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD) in which the recording medium and drive are integrated.

[0020] In the present device 10, the memory 102 and the storage device 104 can also store various information such as log information, information acquired from an external database (not shown) or an external device, information generated by the present device 10, and information used when the present device 10 executes processing. In this case, the memory 102 and the storage device 104 may store, for example, disease-related information, etc., as described below. Note that at least a portion of the information may be stored, for example, in an external server other than the memory 102 and the storage device 104, or may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0021] The device 10 further includes, for example, an input device 105 and an output device 106. Examples of the input device 105 include pointing devices such as a touch panel, track pad, or mouse; a keyboard; imaging means such as a camera or scanner; card readers such as an IC card reader or a magnetic card reader; and audio input means such as a microphone. Examples of the output device 106 include display devices such as an LED display or a liquid crystal display; audio output devices such as a speaker; a printer; etc. In the present disclosure 1, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may be configured as an integrated device, such as a touch panel display.

[0022] Next, an example of the health management support method of the present disclosure will be described with reference to the flowchart of Fig. 3. The health management support method of the present disclosure is implemented as follows, for example, using the device 10 of Fig. 1 or Fig. 2. Note that the health management support method of the present disclosure is not limited to use of the device 10 of Fig. 1 or Fig. 2.

[0023] First, the information acquisition unit 11 acquires disease-related information of the prediction subject (S11, information acquisition step). The disease-related information includes, for example, medical record information, health check information, and subject attribute information (gender, age, etc.). The disease-related information is not limited to the above examples and may be in any form as long as it provides the information necessary for health management support. The disease-related information can be acquired, for example, from a connected external database, via a communication line, or by directly inputting the required information into the device 10, but is not limited to these.

[0024] The disease-related information may also include, for example, disease-related information acquired in real time. In this case, for example, disease-related information collected by a wearable device or the like can be cited. The disease-related information collected by a wearable device or the like can be, for example, factors for predicting the risk of developing a disease, which will be described later. Examples of the wearable device include devices that can measure the factors and are provided on glasses, hats, earrings, earphones, headbands, shirts, sports bras, armbands, bracelets, watches, wristbands, rings, socks, etc.

[0025] FIG. 4 shows an example of a case where the disease-related information includes disease-related information acquired in real time. First, disease-related information necessary for predicting the risk of developing a disease, which will be described later, is acquired through a health checkup or the like. Normally, the acquired disease-related information is not updated until the next health checkup or the like. On the other hand, if the disease-related information is acquired in real time using a wearable device or the like, the disease-related information can be continuously updated until the next health checkup or the like. Note that while this example shows an example in which blood pressure is updated, any disease-related information that can be acquired in real time can be used. In this way, acquiring the disease-related information in real time improves the accuracy of the predicted disease risk and the calculated health index, which will be described later.

[0026] Next, the disease onset prediction unit 12 predicts the disease onset risk of the prediction subject based on the disease-related information (S12, disease onset prediction process). The disease onset risk prediction is performed using analysis results of the disease onset risk based on the state of each factor included in the disease-related information, for example, based on the association between each factor for predicting disease onset and the disease, factors with a high disease onset risk, and the association between the disease to be predicted and other diseases. Factors for predicting disease onset risk include, for example, age, gender, height, weight, good cholesterol (HDL), bad cholesterol (LDL), triglycerides (TG), γGTP, AST (GOT), ALT (GPT), BMI, systolic blood pressure, diastolic blood pressure, blood glucose level, lifestyle habits, medical history, medication information, etc. The factors are not limited to the above-mentioned examples, and other information may be added. Not all of the above-mentioned factors are required, and they may be set appropriately depending on the purpose, etc. Examples of the disease to be predicted include, but are not limited to, brain-related diseases, heart-related diseases, liver-related diseases, and kidney-related diseases, and may be set arbitrarily. Examples of brain diseases include cerebral infarction, cerebral hemorrhage, subarachnoid hemorrhage, etc. Examples of heart diseases include myocardial infarction, angina pectoris, heart failure, etc. Examples of liver diseases include fatty liver, cirrhosis, etc. Examples of kidney diseases include diabetic nephropathy, chronic kidney disease (CKD), etc. The disease risk prediction may be generated for multiple body parts, for example. Furthermore, the disease risk prediction may be, for example, a prediction of the disease risk after an arbitrary period of time has elapsed.

[0027] The disease development risk may be, for example, a disease development risk collected at regular intervals. Here, "regular intervals" is not particularly limited, but means, for example, every regular hour, every regular day, or every regular week. FIG. 5 shows an example of a case where the disease development risk is a disease development risk collected at regular intervals. In FIG. 5, the disease development risk of a prediction subject is recorded for each day, with the horizontal axis representing the date and the vertical axis representing the risk of developing a certain disease.

[0028] The disease onset prediction unit 12 may, for example, extract data related to the disease to be predicted from the disease-related information acquired in real time, and predict the disease onset risk of the prediction subject based on the extracted data. In this case, the disease onset risk prediction may be, for example, a disease onset risk predicted by regression analysis. FIG. 6 shows an example of extracting data related to the disease to be predicted (heart rate) from the disease-related information acquired in real time, and predicting the disease onset risk of the prediction subject based on the extracted data by regression analysis. First, the left diagram of FIG. 6 is a scatter plot with the data related to the disease to be predicted (heart rate) on the horizontal axis and the risk of developing a certain disease on the vertical axis. As shown in the scatter plot of FIG. 6, heart rate data is extracted from the disease-related information acquired in real time, and the relationship between the heart rate data and the risk of developing a certain disease is predicted by regression analysis. Based on the relationship predicted in this way, the disease onset risk is predicted, for example, from heart rates collected at regular intervals (every fixed time in the right diagram of FIG. 6).

[0029] The disease onset prediction unit 12 may be a trained model (disease onset prediction model). In this case, the disease onset prediction model predicts the disease onset risk of the prediction subject based on the disease-related information. The disease onset prediction model is, for example, a trained model that outputs the disease onset risk of the prediction subject when the disease-related information is input. The disease onset prediction model can also be said to be a trained model that is machine-learned based on, for example, multiple pieces of disease-related information and multiple pieces of diagnostic information, and causes a computer to function to predict the disease onset risk of the prediction subject. The multiple pieces of disease-related information may or may not include disease-related information of the prediction subject. The multiple pieces of diagnostic information may or may not include disease-related information of the prediction subject. The disease onset prediction model may be, for example, a trained model that uses a Cox proportional hazards model. Examples of covariates used in the Cox proportional hazards model include age, sex, height, weight, BMI, medication information (hypertension, diabetes, dyslipidemia), lifestyle habits (weight change in the past year, weight change since age 20, smoking, exercise, daily walking / physical activity, walking speed, drinking frequency, alcohol amount, sleep quality, late-night snacks, snacks, chewing meals, late dinner, skipping breakfast, eating meals other than the three meals of breakfast, lunch, and dinner), blood test results (HDL, LDL, TG, γGTP, AST (GOT), ALT (GPT), GLU, UA, HbA1c), blood pressure (systolic blood pressure, diastolic blood pressure), and waist circumference.

[0030] Next, the health index calculation unit 13 calculates a health index for each of the prediction subjects based on the disease development risk (S13, health index calculation step). The health index for each prediction subject means, for example, a health index individualized for each of the prediction subjects. The health index may include, for example, a distribution of disease development risk and a threshold. The distribution of disease development risk is, for example, a normal distribution obtained based on the disease development risk. The threshold is, for example, a standard deviation obtained based on the disease development risk. Furthermore, the health index may be, for example, a score calculated based on the disease development risk. The score may be a numerical score, or may be a score ranked by a symbol or the like.

[0031] FIG. 7 shows an example of a calculated health index. As shown in the left diagram of FIG. 7, the disease development risk is recorded at regular intervals. Furthermore, as shown in the right diagram of FIG. 7, a normal distribution can be obtained based on the recorded disease development risk. In the case of FIG. 7, the mean value ± 2 SD (standard deviation) is set as the threshold. As shown in FIG. 7, risk can be detected when the recorded disease development risk deviates from the set threshold.

[0032] Thereafter, the information output unit 14 outputs disease onset prediction information based on the health index (S14, information output step), and ends the process. The output disease onset prediction information may be, for example, the distribution, threshold, score, etc. described above. The disease onset prediction information may be, for example, disease onset prediction information output based on the threshold. Examples of disease onset prediction information output based on the threshold include warning information due to deviation from the threshold and praise information due to not deviation from the threshold. The warning information and the praise information may be, for example, information in the form of text or audio.

[0033] The information output unit 14 may further output statistical information on a disease. The statistical information may be, for example, statistical information on a subject other than the predicted subject, or on the predicted subject. Examples of the statistical information on a subject other than the predicted subject include a disease onset risk, a health index, etc. calculated based on disease-related information on a subject other than the predicted subject. Furthermore, the statistical information on a subject other than the predicted subject may be, for example, statistical information on attributes (e.g., age, generation, gender, etc.) that are the same as or similar to those of the predicted subject. The statistical information on the predicted subject may be, for example, a health index for each predicted subject calculated based on a past disease onset risk predicted by the health management support device disclosed herein. The past disease onset risk is, for example, a disease onset risk in cases where the subject was in good health or poor health in the past. The output may be, for example, a numerical score, a score ranked by symbols, etc., a normal distribution calculated based on disease-related information on a subject other than the predicted subject or a disease onset risk calculated based on disease-related information on the predicted subject, a standard deviation calculated based on the disease onset risk on a subject other than the predicted subject or the disease-related information on the predicted subject, or the like. By outputting the statistical information, for example, the predicted subject can compare their own disease onset risk and health index with general disease onset risk and health index. FIG. 8 is a diagram showing an example of output statistical information. As shown in the left diagram of FIG. 8, disease onset prediction information for the predicted subject and statistical information for other subjects may be output. Furthermore, as shown in the right diagram of FIG. 8, statistical information for the predicted subject when they have had good health in the past and statistical information for the predicted subject when they have had poor health in the past may be output as the statistical information for the predicted subject. The output may be, for example, displayed on a dedicated monitor connected as an external device. Note that the output is not limited thereto and may be, for example, transmitted to a terminal owned by the predicted subject via a communication line or output to an external database, or may be arbitrarily set according to the intended use.

[0034] [Embodiment 2] The program of the present disclosure is a program for causing a computer to execute each of the steps of the present disclosure described above. Specifically, the program of the present disclosure is a program for causing a computer to execute, for example, an information acquisition procedure, a disease onset prediction procedure, a health index calculation procedure, and an information output procedure.

[0035] Furthermore, the program of the present disclosure can also be said to be a program that causes a computer to function as, for example, an information acquisition procedure, a disease onset prediction procedure, a health index calculation procedure, and an information output procedure.

[0036] The program of the present disclosure can be incorporated by reference to the descriptions of the health management support device and health management support method according to the first embodiment of the present disclosure. For example, the "procedure" in each of the steps can be replaced with "processing." The program of the present disclosure may also be recorded on a computer-readable recording medium. The recording medium may be, for example, a non-transitory computer-readable storage medium. The recording medium is not particularly limited, and examples thereof include random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., solid state drive (SSD), USB flash memory, SD / SDHC card, etc.), optical disc (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disk (MO), floppy disk (FD), etc. Furthermore, the program of the present disclosure (e.g., a programming product or a program product) may be distributed from an external computer, for example. The "distribution" may be, for example, distribution via a communication network or distribution via a device connected via a wire. The program of the present disclosure may be installed and executed on the device to which it is distributed, or may be executed without being installed.

[0037] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure.

[0038] <Supplementary Notes> Some or all of the above embodiments can be described as in the following supplementary notes, but are not limited to the following. (Supplementary Note 1) A health management support device including an information acquisition unit, a disease onset prediction unit, a health index calculation unit, and an information output unit, wherein the information acquisition unit acquires disease-related information of a prediction subject, the disease onset prediction unit predicts a disease onset risk of the prediction subject based on the disease-related information, the health index calculation unit calculates a health index for each prediction subject based on the disease onset risk, and the information output unit outputs disease onset prediction information based on the health index. (Supplementary Note 2) The health management support device according to Supplementary Note 1, wherein the disease onset risk is a disease onset risk collected at regular intervals. (Supplementary Note 3) The health management support device according to Supplementary Note 1 or 2, wherein the disease-related information includes disease-related information acquired in real time. (Supplementary Note 4) The health management support device of Supplementary Note 3, wherein the disease onset prediction unit extracts data related to the disease to be predicted from the disease-related information acquired in real time, and predicts the disease onset risk of the person to be predicted based on the extracted data. (Supplementary Note 5) The health management support device of any of Supplements 1 to 4, wherein the health index includes a distribution of the disease onset risk and a threshold value. (Supplementary Note 6) The health management support device of Supplementary Note 5, wherein the disease onset prediction information is disease onset prediction information output based on the threshold value. (Supplementary Note 7) The health management support device of any of Supplements 1 to 6, wherein the information output unit further outputs statistical information on a disease. (Supplementary Note 8) The health management support device of Supplementary Note 7, wherein the statistical information is statistical information of a person other than the person to be predicted, or statistical information of the person to be predicted. (Supplementary Note 9) A health management support method comprising an information acquisition step, a disease onset prediction step, a health index calculation step, and an information output step, wherein the information acquisition step acquires disease-related information of a prediction subject, the disease onset prediction step predicts a disease onset risk of the prediction subject based on the disease-related information, the health index calculation step calculates a health index for each prediction subject based on the disease onset risk, and the information output step outputs disease onset prediction information based on the health index. (Supplementary Note 10) The health management support method according to Supplementary Note 9, wherein the disease onset risk is a disease onset risk collected at regular intervals.(Supplementary Note 11) The health management support method according to Supplementary Note 9 or 10, wherein the disease-related information includes disease-related information acquired in real time. (Supplementary Note 12) The health management support method according to Supplementary Note 11, wherein the disease onset prediction step extracts data related to the disease to be predicted from the disease-related information acquired in real time, and predicts the disease onset risk of the person to be predicted based on the extracted data. (Supplementary Note 13) The health management support method according to any of Supplements 9 to 12, wherein the health index includes a distribution of the disease onset risk and a threshold value. (Supplementary Note 14) The health management support method according to Supplementary Note 13, wherein the disease onset prediction information is disease onset prediction information output based on the threshold value. (Supplementary Note 15) The health management support method according to any of Supplements 9 to 14, wherein the information output step further outputs statistical information on a disease. (Supplementary Note 16) The health management support method according to Supplementary Note 15, wherein the statistical information is statistical information of a person other than the person to be predicted, or statistical information of the person to be predicted. (Supplementary Note 17) A program for causing a computer to execute each of the steps, including an information acquisition step, a disease onset prediction step, a health index calculation step, and an information output step, wherein the information acquisition step acquires disease-related information of a subject to be predicted, the disease onset prediction step predicts a disease onset risk of the subject to be predicted based on the disease-related information, the health index calculation step calculates a health index for each subject to be predicted based on the disease onset risk, and the information output step outputs disease onset prediction information based on the health index. (Supplementary Note 18) The program according to Supplementary Note 17, wherein the disease onset risk is a disease onset risk collected at regular intervals. (Supplementary Note 19) The program according to Supplementary Note 17 or 18, wherein the disease-related information includes disease-related information obtained in real time. (Supplementary Note 20) The program according to Supplementary Note 19, wherein the disease onset prediction step extracts data related to the disease of the subject to be predicted from the disease-related information obtained in real time, and predicts the disease onset risk of the subject to be predicted based on the extracted data. (Supplementary Note 21) The program according to any one of Supplementary Notes 17 to 20, wherein the health index includes a distribution and a threshold of the disease development risk.(Supplementary Note 22) The program according to Supplementary Note 21, wherein the disease onset prediction information is disease onset prediction information output based on the threshold. (Supplementary Note 23) The program according to any of Supplements 17 to 22, wherein the information output step further outputs statistical information for the disease. (Supplementary Note 24) The program according to Supplementary Note 23, wherein the statistical information is statistical information of a person other than the person to be predicted, or statistical information of the person to be predicted. (Supplementary Note 25) A computer-readable recording medium having recorded thereon a program for causing a computer to execute each of the steps, including an information acquisition step, a disease onset prediction step, a health index calculation step, and an information output step, wherein the information acquisition step acquires disease-related information of the person to be predicted, the disease onset prediction step predicts a disease onset risk of the person to be predicted based on the disease-related information, the health index calculation step calculates a health index for each person to be predicted based on the disease onset risk, and the information output step outputs disease onset prediction information based on the health index. (Appendix 26) The recording medium according to Appendix 25, wherein the disease onset risk is a disease onset risk collected at regular intervals. (Appendix 27) The recording medium according to Appendix 25 or 26, wherein the disease-related information includes disease-related information acquired in real time. (Appendix 28) The recording medium according to Appendix 27, wherein the disease onset prediction procedure extracts data related to the disease to be predicted from the disease-related information acquired in real time, and predicts the disease onset risk of the person to be predicted based on the extracted data. (Appendix 29) The recording medium according to any of Appendixes 25 to 28, wherein the health index includes a distribution and a threshold value of the disease onset risk. (Appendix 30) The recording medium according to Appendix 29, wherein the disease onset prediction information is disease onset prediction information output based on the threshold value. (Appendix 31) The recording medium according to any of Appendixes 25 to 30, wherein the information output procedure further outputs statistical information on the disease. (Appendix 32) The recording medium according to Appendix 31, wherein the statistical information is statistical information of a person other than the person to be predicted, or statistical information of the person to be predicted.

[0039] This application claims priority based on Japanese Patent Application No. 2023-180786, filed October 20, 2023, the disclosure of which is incorporated herein in its entirety by reference.

[0040] According to the present disclosure, it is possible to support health management based on individualized health indices. The fields to which the present disclosure can be applied are not limited, and the present disclosure can be applied to a wide range of fields using health management support devices.

[0041] REFERENCE SIGNS LIST 10 Health management support device 11 Information acquisition unit 12 Disease onset prediction unit 13 Health index calculation unit 14 Information output unit 101 Central processing unit 102 Memory 103 Bus 104 Storage device 105 Input device 106 Output device 107 Communication device

Claims

1. A health management support device including an information acquisition unit, a disease onset prediction unit, a health index calculation unit, and an information output unit, wherein the information acquisition unit acquires disease-related information of a prediction subject, the disease onset prediction unit predicts a disease onset risk of the prediction subject based on the disease-related information, the health index calculation unit calculates a health index for each prediction subject based on the disease onset risk, and the information output unit outputs disease onset prediction information based on the health index.

2. The health care support device according to claim 1, wherein said disease development risk is collected at regular intervals.

3. The health care support device according to claim 1 or 2, wherein the disease-related information includes disease-related information acquired in real time.

4. A health management support device as described in claim 3, wherein the disease onset prediction unit extracts data related to the disease to be predicted from the disease-related information acquired in real time, and predicts the disease onset risk of the person to be predicted based on the extracted data.

5. A health management support device according to claim 1 or 2, wherein the health index includes a distribution and a threshold value of the disease onset risk.

6. The health care support device according to claim 5, wherein the disease onset prediction information is output based on the threshold value.

7. The health management support device according to claim 1 or 2, wherein the information output section further outputs statistical information on the disease.

8. The health management support device according to claim 7, wherein the statistical information is statistical information of a subject other than the subject to be predicted, or statistical information of the subject to be predicted.

9. A health management support method comprising an information acquisition step, a disease onset prediction step, a health index calculation step, and an information output step, wherein the information acquisition step acquires disease-related information of a prediction subject, the disease onset prediction step predicts a disease onset risk of the prediction subject based on the disease-related information, the health index calculation step calculates a health index for each prediction subject based on the disease onset risk, and the information output step outputs disease onset prediction information based on the health index.

10. The health care support method according to claim 9, wherein the disease development risk is collected at regular intervals.

11. The health care support method according to claim 9 or 10, wherein the disease-related information includes disease-related information acquired in real time.

12. A health management support method as described in claim 11, wherein the disease onset prediction process extracts data related to the disease to be predicted from the disease-related information acquired in real time, and predicts the disease onset risk of the person to be predicted based on the extracted data.

13. A health management support method according to claim 9 or 10, wherein the health index includes a distribution and a threshold value of the disease onset risk.

14. The health care support method according to claim 13, wherein the disease onset prediction information is output based on the threshold value.

15. The health care support method according to claim 9 or 10, wherein said information output step further outputs statistical information regarding the disease.

16. The health management support method according to claim 15, wherein the statistical information is statistical information of a person other than the person to be predicted, or statistical information of the person to be predicted.

17. A program for causing a computer to execute each of the above steps, comprising an information acquisition step, a disease onset prediction step, a health index calculation step, and an information output step, wherein the information acquisition step acquires disease-related information of a prediction subject, the disease onset prediction step predicts a disease onset risk of the prediction subject based on the disease-related information, the health index calculation step calculates a health index for each prediction subject based on the disease onset risk, and the information output step outputs disease onset prediction information based on the health index.

18. The program according to claim 17, wherein the disease development risk is collected at regular intervals.

19. The program of claim 17 or 18, wherein the disease-related information includes disease-related information obtained in real time.

20. A computer-readable recording medium having recorded thereon a program for causing a computer to execute each of the steps, the computer-readable recording medium including an information acquisition step, a disease onset prediction step, a health index calculation step, and an information output step, the information acquisition step acquiring disease-related information of a prediction subject, the disease onset prediction step predicting a disease onset risk of the prediction subject based on the disease-related information, the health index calculation step calculating a health index for each prediction subject based on the disease onset risk, and the information output step outputting disease onset prediction information based on the health index.

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