Health information evaluation assistance program, health information evaluation assistance device, health information evaluation assistance method, and recording medium

Abstract

Provided is a health information evaluation assistance program capable of evaluating health information even when information necessary for evaluating the health information is changed.　The health information evaluation assistance program of the present disclosure includes an information acquisition procedure, a prediction procedure, an evaluation procedure, and an output procedure. The information acquisition procedure involves acquiring information on a prediction target person. The prediction procedure involves predicting information necessary for evaluating health information from the information of the prediction target person. The evaluation procedure involves evaluating the health information about the prediction target person from the information necessary for evaluating the health information. The output procedure involves outputting the evaluated health information. The health information evaluation assistance program is a program that causes a computer to execute each procedure.

Description

Health Information Evaluation Support Program, Health Information Evaluation Support Device, Health Information Evaluation Support Method, and Recording Medium

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

[0002] Patent Document 1 discloses a health diagnosis device including a patient information input processing unit that inputs patient information indicating information that may affect the onset of a disease, obtained for a patient who has undergone a health examination, and a risk prediction processing unit that obtains a risk value indicating the degree of likelihood that the patient will develop a disease, using the input patient information of the patient and a risk parameter obtained from the incidence rate of diseases of past patients.

[0003] Japanese Patent Application Laid-Open No. 2000-262479

[0004] In the health diagnosis device disclosed in Patent Document 1, it is necessary to create a risk prediction processing unit for each disease. As described above, the risk prediction processing unit in this document calculates a risk value related to a disease based on certain information such as patient information and risk parameters. However, when the information for calculating the risk value is changed for some reason, it becomes necessary to newly provide a risk prediction processing unit for calculating the risk value based on the changed information. However, the work of providing a new risk prediction processing unit requires a great deal of labor. This problem is not limited to the prediction of health as disclosed in Patent Document 1, and the same can be said for other devices for performing evaluations related to health.

[0005] Therefore, an object of the present disclosure is to provide a health information evaluation support program, a health information evaluation support device, a health information evaluation support method, and a recording medium that can evaluate health information even when there is a change in the information necessary for evaluating health information.

[0006] To achieve the aforementioned objective, the health information assessment support program of this disclosure includes an information acquisition procedure, a prediction procedure, an evaluation procedure, and an output procedure, wherein the information acquisition procedure acquires information of the person to be predicted; the prediction procedure predicts information necessary for evaluating health information from the information of the person to be predicted; the evaluation procedure evaluates the health information of the person to be predicted from the information necessary for evaluating health information; and the output procedure outputs the evaluated health information. The program is designed to cause a computer to execute each of these procedures.

[0007] The health information evaluation support device disclosed herein includes an information acquisition unit, a prediction unit, an evaluation unit, and an output unit, wherein the information acquisition unit acquires information of a person to be predicted, the prediction unit predicts information necessary for evaluating health information from the information of the person to be predicted, the evaluation unit evaluates the health information of the person to be predicted from the information necessary for evaluating health information, and the output unit outputs the evaluated health information.

[0008] The health information evaluation support method disclosed herein includes an information acquisition step, a risk calculation step, and a risk output step, and includes an information acquisition step, a prediction step, an evaluation step, and an output step, wherein the information acquisition step acquires information of the person to be predicted, the prediction step predicts information necessary for evaluating health information from the information of the person to be predicted, the evaluation step evaluates the health information of the person to be predicted from the information necessary for evaluating health information, and the output step outputs the evaluated health information.

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

[0010] According to this disclosure, health information can be evaluated even if there are changes in the information necessary for evaluating health information.

[0011] Figure 1 is a block diagram showing the configuration of an example of a health information evaluation support device of the present disclosure. Figure 2 is a block diagram showing an example of the hardware configuration of a health information evaluation support device of the present disclosure. Figure 3 is a flowchart showing an example of a procedure in a health information evaluation support program of the present disclosure. Figure 4 is a diagram showing an example of a method for evaluating health information using a health information evaluation support device of the present disclosure. Figure 4(A) is a diagram showing an example of a method for evaluating health information when it includes a prediction unit and an evaluation unit. Figure 4(B) is a diagram showing an example of a method for evaluating health information when it includes multiple prediction units in parallel. Figure 4(C) is a diagram showing an example of a method for evaluating health information when it includes multiple prediction units in series. Figure 4(D) is a diagram showing an example of a method for evaluating health information when it includes multiple evaluation units. Figure 5 is a diagram showing an example of a method for evaluating health information using a health information evaluation support device when it does not include a prediction unit.

[0012] Embodiments of this disclosure will now be described. However, this disclosure is not limited to the embodiments described below. In the following figures, the same parts are denoted by the same reference numerals. Furthermore, unless otherwise specified, the descriptions of each embodiment can be used interchangeably. Furthermore, unless otherwise specified, the configurations of each embodiment can be combined. Also, in the programs of this disclosure described later, the term "procedure" can be read as "process," for example.

[0013] [Embodiment 1] The health information evaluation support program, health information evaluation support device, and health information evaluation support method of the present disclosure will be described.

[0014] The health information assessment support program disclosed herein is a program that causes a computer to execute information acquisition procedures, prediction procedures, evaluation procedures, and output procedures. The health information assessment support program disclosed herein can also be described as a program that causes a computer to function as the information acquisition procedure, prediction procedure, evaluation procedure, and output procedure. Furthermore, the health information assessment support program disclosed herein can also be described as a program that causes a computer to execute each step of the health information assessment support method described later.

[0015] Next, an example of the health information evaluation support device disclosed herein will be explained based on Figures 1 and 2.

[0016] Figure 1 is a block diagram showing an example configuration of a health information evaluation support device 10 (the device 10) of the present disclosure. As shown in Figure 1, the device 10 includes an information acquisition unit 11, a prediction unit 12, an evaluation unit 13, and an output unit 14.

[0017] The device 10 may be, for example, a single device including the aforementioned parts, or it may be a device in which each of the aforementioned parts can be connected via a communication network. Furthermore, the device 10 can be connected to an external device, which will be described later, via the communication network. The communication network is not particularly limited and can use a known network, for example, it may be wired or wireless. Examples of the communication network include an internet connection, WWW (World Wide Web), telephone line, LAN (Local Area Network), SAN (Storage Area Network), DTN (Delay Tolerant Networking), LPWA (Low Power Wide Area), L5G (Local 5G), etc. Examples of wireless communication include Wi-Fi®, Bluetooth®, local 5G, LPWA, etc. The wireless communication may be in the form of direct communication between devices (Ad Hoc communication), infrastructure communication, indirect communication via an access point, etc. The device 10 may be incorporated into a server as a system, for example. Alternatively, the device 10 may be a personal computer (PC, e.g., desktop or notebook), smartphone, tablet terminal, digital signage, etc., on which the program disclosed herein is installed. The device 10 may be in the form of cloud computing or edge computing, for example, in which at least one of the above parts is on a server and the other parts are on a terminal.

[0018] Figure 2 illustrates a block diagram of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, memory 102, bus 103, storage device 104, input device 105, output device 106, communication device 107, etc. Each part of the device 10 is interconnected via the bus 103 through its respective interface (I / F).

[0019] The central processing unit 101 operates in coordination with other components via controllers (system controller, I / O controller, etc.) and is responsible for the overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program disclosed herein or 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 prediction unit 12, an evaluation unit 13, and an output unit 14. The central processing unit 101 may include a CPU, a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), or a combination thereof as its arithmetic unit.

[0020] Bus 103 can also be connected to external devices, for example. Examples of such external devices include external storage devices such as external databases, printers, external input devices, external display devices, and external imaging devices. The device 10 can be connected to an external network (the aforementioned communication network) by a communication device 107 connected to bus 103, for example, and can also be connected to other devices via the external network.

[0021] Memory 102 may be, for example, main memory. When the central processing unit 101 performs processing, memory 102 reads various operational programs, such as the program of this disclosure, stored in the storage device 104 (described later), and the central processing unit 101 receives data from memory 102 and executes the program. The main memory may be, for example, RAM (random access memory). Alternatively, memory 102 may be, for example, ROM (read-only memory).

[0022] The storage device 104 is also called an auxiliary storage device, for example, in relation to the main memory (primary memory). As described above, the storage device 104 stores an operating program including the program of this disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive for reading and writing to the recording medium. The recording medium is not particularly limited and may be internal or external, for example, an HD (hard disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, etc. The storage device 104 may be, for example, a hard disk drive (HDD) in which the recording medium and the drive are integrated, or a solid state drive (SSD).

[0023] In this device 10, the memory 102 and storage device 104 can also store various types of information, such as log information, information obtained from an external database (not shown) or external devices, information generated by this device 10, and information used by this device 10 when executing processing. In this case, the memory 102 and storage device 104 may store, for example, information about the person to be predicted, information necessary for evaluating health information, etc. At least some of the information may be stored on an external server other than the memory 102 and storage device 104, or it may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0024] The device 10 further includes, for example, an input device 105 and an output device 106. The input device 105 may include, for example, a pointing device such as a touch panel, trackpad, or mouse; a keyboard; imaging means such as a camera or scanner; a card reader such as an IC card reader or magnetic card reader; an audio input means such as a microphone; and so on. The output device 106 may include, for example, a display device such as an LED display or liquid crystal display; an audio output device such as a speaker; a printer; and so on. In this disclosure, 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 unit, such as a touch panel display.

[0025] First, an example of the processing of the health information assessment support program disclosed herein will be specifically explained based on Figure 3. Figure 3 is a flowchart showing an example of each step in the health information assessment support program disclosed herein.

[0026] First, the information acquisition unit 11 acquires information about the person to be predicted (S11, information acquisition procedure). The information about the person to be predicted may include, for example, physical information, examination information, lifestyle information, and medical history of the person to be predicted. The physical information may include, for example, height, weight, waist circumference, BMI (Body Mass Index), age, sex, and weight changes over a certain period. The examination information may include, for example, HbA1c (hemoglobin A1c), fasting blood glucose, HDL (High Density Lipoprotein), LDL (Low Density Lipoprotein), TG (triglyceride), γGTP (γ-glutamyl transpeptidase), AST (aspartate aminotransferase), ALT (alanine aminotransferase), uric acid, systolic blood pressure, and diastolic blood pressure. The lifestyle information may include, for example, habits related to smoking, drinking, exercise, sleep, and diet. The aforementioned medical history may be information obtained from at least one of the following: surgical history, hospitalization history, outpatient history, medication history, and drug prescription history. The information of the person to be predicted may be information obtained from at least one of the following: health checkups, medical interviews, home tests, wearable devices, online diagnoses, healthcare apps, outpatient records, and health records. The information of the person to be predicted may be at least one of past and hypothetical information. The past information may be information of the person to be predicted in the past. The hypothetical information may be information of the person to be predicted based on assumptions. Specifically, for example, when acquiring exercise habits as information of the person to be predicted, it may be information indicating assumptions such as "the person did not actually have an exercise habit, but hypothetically had an exercise habit." The information acquisition unit 11 may, for example, acquire the information of the person to be predicted from information input by the input device 105 of this device 10, or from information stored in the memory 102 or storage device 104 of this device 10, or from log information, an external database, or an external device.

[0027] Next, the prediction unit 12 predicts information necessary for evaluating health information from the information of the person to be predicted (S12, prediction procedure). Examples of information necessary for evaluating health information include predicted physical information and predicted test information of the person to be predicted. Examples of predicted physical information include weight, waist circumference, BMI (Body Mass Index), and weight changes over a certain period. Examples of predicted test information include HbA1c (hemoglobin A1c), fasting blood glucose, HDL (High Density Lipoprotein) value, LDL (Low Density Lipoprotein), TG (triglyceride), γGTP (γ-glutamyl transpeptidase), AST (aspartate aminotransferase), ALT (alanine aminotransferase), uric acid, systolic blood pressure, and diastolic blood pressure. The prediction unit 12 may, for example, predict information necessary for evaluating health information based on the past and provisional settings from the information of the person to be predicted.

[0028] The prediction unit 12 may be, for example, a trained model generated by machine learning using the information of the person to be predicted. Alternatively, the prediction unit 12 may be, for example, a trained model that predicts the information necessary for evaluating the health information when the information of the person to be predicted is input. The trained model may be, for example, trained based on a multivariate regression model assuming a Gaussian distribution. The information necessary for evaluating the health information may be predicted by, for example, the following equation (1), but is not limited to this: y = X'β + ε (1) In equation (1), y is an n × 1 current test value vector, n is the sample size, X' is an n × m covariate matrix, and x' １ = 1, x' ｋ∈S∪ΔS, k=2,3,...,m, where m-1 is the number of covariates that are statistically significant in the simple regression model and also selected using the stepwise method (two-way / AIC), S is the set of covariates including the test value from one year ago, current age, and current sex, ΔS is the set of changes in lifestyle and medication information from one year ago to the present, β is the m×1 coefficient vector, and ε is the n×1 error term vector.

[0029] The prediction unit 12 may include, for example, multiple prediction units. These multiple prediction units may be arranged in parallel, in series, or both. For example, if there are multiple pieces of information necessary for evaluating the health information, the multiple prediction units may predict the multiple pieces of information necessary for evaluating the health information. The prediction unit 12 may also predict the information necessary to predict the information necessary for evaluating the health information. In other words, the prediction unit 12 may predict the information necessary for evaluating the health information in multiple stages.

[0030] Next, the evaluation unit 13 evaluates the health information of the person to be predicted from the information necessary for evaluating the health information (S13, evaluation procedure). The evaluation unit 13 may, for example, evaluate the health information of the person to be predicted based on the past and hypothetical information from the information necessary for evaluating the health information. Examples of the evaluation of health information include prediction of disease onset risk, prediction of health status, and healthcare suggestions. The prediction of disease onset risk includes, for example, the prediction of the risk of developing any disease. Examples of the aforementioned diseases include, but are not limited to, type 2 (non-insulin-dependent) diabetes mellitus (NIDDM), lipoprotein metabolism disorders and other lipid disorders, purine and pyrimidine metabolism disorders, sleep disorders, essential (primary) hypertension, acute myocardial infarction, chronic ischemic heart disease, atrial fibrillation and flutter, heart failure, cerebral infarction, atherosclerosis, aortic aneurysm and dissection, alcoholic liver disease, hepatic fibrosis and cirrhosis, gout, knee osteoarthritis, osteoporosis (without pathological fracture), chronic kidney disease, and angina pectoris. The prediction of the health status includes, for example, the prediction of good or bad health status regardless of whether or not a disease has developed. The healthcare proposals include, for example, suggestions for lifestyle improvements to avoid disease onset or improve health status.

[0031] The evaluation unit 13 may be, for example, a trained model generated by machine learning using the information necessary for evaluating the health information. Alternatively, the evaluation unit 13 may be, for example, a trained model that evaluates the health information of the target person when the information necessary for evaluating the health information is input. The trained model may be, for example, trained based on a Cox proportional hazards model. The health information of the target person may be evaluated by, for example, the following formula (2), but is not limited to this. In the above formula, t is any period, and S ０ is the baseline survival function, X is the test value, β is the regression coefficient of the predictive model, and X-bar is the mean of the test value.

[0032] The evaluation unit 13 may include, for example, multiple evaluation units. For example, when evaluating multiple pieces of health information, multiple evaluation units may evaluate multiple pieces of health information.

[0033] Next, the output unit 14 outputs the evaluated health information (S14, output procedure). The output unit 14 may output, for example, information about the person to be predicted, information necessary for evaluating the health information, and causal relationships between at least two of the health information from the evaluated health information. The format of the output is not particularly limited, but examples include absolute evaluation, relative evaluation, numerical values, evaluation results based on thresholds, text, graphs, etc. The output may be output to, for example, an output device 106 provided by this device 10, or to an output device provided by another device other than this device 10.

[0034] According to the health information evaluation support program disclosed herein, information of the person to be predicted is obtained by the information acquisition procedure, information necessary for evaluating health information is predicted from the information of the person to be predicted by the prediction procedure, the health information of the person to be predicted is evaluated from the information necessary for evaluating health information by the evaluation procedure, and the evaluated health information is output by the output procedure. This makes it possible to evaluate health information even if there are changes in the information necessary for evaluating health information.

[0035] Next, we will explain the methods for supporting the evaluation of health information in this disclosure.

[0036] The health information evaluation support method disclosed herein can be implemented by referring to the descriptions in the health information evaluation support program and health information evaluation support device disclosed herein. The health information evaluation support method disclosed herein is a method implemented by, for example, replacing each "procedure" in the program disclosed herein with a "process". Specifically, the health information evaluation support method disclosed herein includes an information acquisition process, a prediction process, an evaluation process, and an output process. The health information evaluation support method disclosed herein can be implemented, for example, using the device 10 in Figure 1. However, the health information evaluation support method disclosed herein is not limited to the use of the device 10 in Figure 1.

[0037] [Embodiment 2] An example of a method for evaluating health information using the health information evaluation support device of the present disclosure will be explained with specific examples shown in Figures 4 and 5. In this disclosure, an example using the device 10 is shown, but the invention is not limited thereto. In this embodiment, the prediction of disease onset risk is given as an example of health information evaluation, but the invention is not limited thereto.

[0038] First, as shown in Figure 4(A), the device 10 acquires information about the person to be predicted by an information acquisition unit (not shown). This information may include, for example, physical information such as height and weight, test information such as blood tests and blood pressure tests, lifestyle information such as exercise habits, and medical history. Next, the prediction unit 20 predicts information necessary for evaluating health information (disease onset risk) from the acquired information about the person to be predicted. The prediction by the prediction unit 20 may be performed, for example, by a trained model. This information necessary for evaluating health information (disease onset risk) may include, for example, predicted physical information such as the person's weight and waist circumference, and predicted test information such as blood tests and blood pressure tests. Next, the evaluation unit 30 evaluates the health information of the person to be predicted from the information necessary for evaluating health information (disease onset risk). The evaluation by the evaluation unit 30 may be performed, for example, by a trained model. Afterward, the evaluated health information (disease onset risk) is output by an output unit (not shown).

[0039] As shown in Figure 5, for example, if the system does not include the prediction unit 20 and only includes the evaluation unit 30, the evaluation unit 30 needs to be modified (recreated) when the information necessary for evaluating health information changes (for example, when the items of a health checkup change and a certain test item can no longer be obtained). On the other hand, as shown in Figure 4(A), even if the information of the person to be predicted that can be obtained changes (for example, when the items of a health checkup change and a certain test item can no longer be obtained), the evaluation unit 30 can be used as before by modifying (recreating) only the prediction unit 20.

[0040] Here, as shown in FIG. 4(B), the prediction unit 20 may include a plurality of prediction units (prediction units 20a, 20b, 20c,...) in parallel. In FIG. 4(B), each prediction unit predicts information necessary for the evaluation of the health information used in the evaluation unit 30. For example, the prediction unit 20a may predict physical information, and the prediction unit 20b may predict examination information. Also, for example, the prediction unit 20a may predict weight, and the prediction unit 20b may predict abdominal circumference. According to FIG. 4(B), since the information necessary for the evaluation of the predicted health information is subdivided, even when a change occurs in the prediction unit, the cost associated with the change can be reduced more than in the case of FIG. 4(A).

[0041] Also, as shown in FIG. 4(C), the prediction unit 20 may include a plurality of prediction units (prediction units 20a1... prediction units 20a5... 20ax) in series. In FIG. 4(C), for each prediction unit to predict information necessary for the evaluation of the health information (disease onset risk) predicted by the prediction unit 20az, prediction is performed in multiple stages starting from the prediction unit 20a1. For example, to predict blood test information in the prediction unit 20ax, the prediction unit 20a1 inputs information on exercise habits to predict the future continuation state of exercise, the prediction unit 20a2 predicts the future weight from the predicted future continuation state of exercise, and then, after several stages of prediction from the predicted future weight, finally the prediction unit 20ax predicts the blood test information. According to FIG. 4(C), by combining various prediction units, information necessary for the evaluation of the ultimately required health information can be obtained.

[0042] Also, as shown in FIG. 4(D), the evaluation unit 30 may include a plurality of evaluation units (evaluation units 30a, 30b, 30c,...). In FIG. 4(D), each evaluation unit evaluates a plurality of health information (health information a, b, c,...). For example, the evaluation unit 30a may evaluate the onset risk of heart failure, the evaluation unit 30b may evaluate the onset risk of cerebral infarction, and the evaluation unit 30c may evaluate the onset risk of sleep disorder. According to FIG. 4(D), for example, various health information can be evaluated collectively.

[0043] Note that the aspects disclosed in FIG. 4 can be combined with each other.

[0044] Incidentally, in FIG. 4, an output unit (not shown) may output, for example, a causal relationship between the information of the prediction target person and the health information. As the information of the prediction target person, for example, when including temporarily set information in addition to the past information of the prediction target person, in the simulation, when the health information changes by improving lifestyle habits or the like, it is possible to explain the reason from the estimated value of the information necessary for the evaluation of the predicted health information.

[0045] For example, although the past information of the prediction target person was "not exercising", when including "performing light sweating exercise for 30 minutes or more" as the temporarily set information, as the information necessary for the evaluation of the predicted health information, it can be seen that the estimated values of "triglyceride, LDL, abdominal circumference" are improved by "performing light sweating exercise for 30 minutes or more" (the causal relationship between the information of the prediction target person and the information necessary for the evaluation of the health information). Also, it can be seen that as the health information, "the risk of developing dyslipidemia decreases" (the causal relationship between the information of the prediction target person and the health information).

[0046] Further, for example, although the past information of the prediction target person was "drinking alcohol every day", when including "reducing the amount of alcohol consumed" as the temporarily set information, as the information necessary for the evaluation of the predicted health information, it can be seen that the estimated values of "weight, γ-GTP, AST" are improved by "reducing the amount of alcohol consumed" (the causal relationship between the information of the prediction target person and the information necessary for the evaluation of the health information). Also, it can be seen that as the health information, "the risk of developing alcoholic liver disease decreases" (the causal relationship between the information of the prediction target person and the health information).

[0047] [Embodiment 3] The program of the present disclosure may be recorded on, for example, a computer-readable storage medium. The storage medium is, for example, a non-transitory computer-readable storage medium. The storage medium is not particularly limited and includes, for example, random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., 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. The program of the present disclosure (for example, also referred to as a programming product or program product) may be delivered, for example, from an external computer. The "delivery" may be, for example, delivered via a communication network or delivered via a wired device. The program of the present disclosure may be installed and executed on the delivered device, or it may be executed without being installed.

[0048] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. The configuration and conditions of the present disclosure can be modified in various ways that can be understood by those skilled in the art within the scope of the present disclosure.

[0049] <Notes> Some or all of the above embodiments may be described as follows, but are not limited to the following. (Note 1) A health information evaluation support program for causing a computer to execute each of the following procedures, including an information acquisition procedure, a prediction procedure, an evaluation procedure, and an output procedure, wherein the information acquisition procedure acquires information of a person to be predicted, the prediction procedure predicts information necessary for evaluating health information from the information of the person to be predicted, the evaluation procedure evaluates the health information of the person to be predicted from the information necessary for evaluating health information, and the output procedure outputs the evaluated health information. (Note 2) The health information evaluation support program according to Note 1, wherein the prediction procedure includes a plurality of prediction procedures, and / or the evaluation procedure includes a plurality of evaluation procedures. (Note 3) The health information evaluation support program according to Note 1 or 2, wherein the information of the person to be predicted includes at least one of the person to be predicted's physical information, examination information, lifestyle information, and medical history, and the information of the person to be predicted is past or hypothetical information. (Note 4) A health information evaluation support program according to any one of Notes 1 to 3, wherein the information necessary for evaluating the health information includes at least one of the predicted physical information of the person to be predicted and the predicted examination information. (Note 5) A health information evaluation support program according to any one of Notes 1 to 4, wherein the information of the person to be predicted includes past and hypothetical information, the prediction procedure predicts the information necessary for evaluating the health information based on the past and hypothetical information from the information of the person to be predicted, the evaluation procedure evaluates the health information of the person to be predicted based on the past and hypothetical information from the information necessary for evaluating the health information, and the output procedure outputs the causal relationship between the information of the person to be predicted, the information necessary for evaluating the health information, and at least two of the health information from the evaluated health information. (Note 6) A health information evaluation support program according to any one of Notes 1 to 5, wherein the evaluation of the health information includes at least one of the prediction of disease onset risk, prediction of health status, and healthcare proposals.(Note 7) A health information evaluation support program according to any one of Notes 1 to 6, wherein the prediction procedure is a trained model generated by machine learning using the information of the person to be predicted, and / or the evaluation procedure is a trained model generated by machine learning using the information necessary for evaluating the health information. (Note 8) A health information evaluation support device including an information acquisition unit, a prediction unit, an evaluation unit, and an output unit, wherein the information acquisition unit acquires information of the person to be predicted, the prediction unit predicts information necessary for evaluating the health information from the information of the person to be predicted, the evaluation unit evaluates the health information of the person to be predicted from the information necessary for evaluating the health information, and the output unit outputs the evaluated health information. (Note 9) A health information evaluation support device according to Note 8, wherein the prediction unit includes a plurality of prediction units, and / or the evaluation unit includes a plurality of evaluation units. (Note 10) The health information evaluation support device according to Note 8 or 9, wherein the information of the person to be predicted includes at least one of the person to be predicted's physical information, examination information, lifestyle information, and medical history, and the information of the person to be predicted is past or provisional information. (Note 11) The health information evaluation support device according to any one of Notes 8 to 10, wherein the information necessary for evaluating the health information includes at least one of the person to be predicted's predicted physical information and predicted examination information. (Note 12) The health information evaluation support device according to any one of Notes 8 to 11, wherein the information of the person to be predicted includes past and provisional information, the prediction unit predicts the information necessary for evaluating the health information based on the past and provisional information from the person to be predicted, the evaluation unit evaluates the health information of the person to be predicted based on the information necessary for evaluating the health information based on the past and provisional information, and the output unit outputs the causal relationship between the person to be predicted, the information necessary for evaluating the health information, and at least two of the health information from the evaluated health information. (Note 13) A health information evaluation support device according to any one of Notes 8 to 12, wherein the evaluation of health information includes at least one of predicting the risk of disease onset, predicting health status, and proposing healthcare.(Note 14) The health information evaluation support device according to any one of Notes 8 to 13, wherein the prediction unit is a trained model generated by machine learning using the information of the person to be predicted, and / or the evaluation unit is a trained model generated by machine learning using the information necessary for evaluating the health information. (Note 15) A health information evaluation support method comprising an information acquisition step, a prediction step, an evaluation step, and an output step, wherein the information acquisition step acquires information of the person to be predicted, the prediction step predicts information necessary for evaluating health information from the information of the person to be predicted, the evaluation step evaluates the health information of the person to be predicted from the information necessary for evaluating health information, and the output step outputs the evaluated health information, each of which is performed by a computer. (Note 16) The health information evaluation support method according to Note 15, wherein the prediction step includes a plurality of prediction steps, and / or the evaluation step includes a plurality of evaluation steps. (Note 17) The health information evaluation support method described in Note 15 or 16, wherein the information of the person to be predicted includes at least one of the person to be predicted's physical information, examination information, lifestyle information, and medical history, and the information of the person to be predicted is past or provisional information. (Note 18) The health information evaluation support method described in any one of Notes 15 to 17, wherein the information necessary for evaluating the health information includes at least one of the person to be predicted's predicted physical information and predicted examination information. (Note 19) A health information evaluation support method according to any one of Notes 15 to 18, wherein the information of the person to be predicted includes past and hypothetical information, the prediction step predicts information necessary for evaluating the health information based on the past and hypothetical information from the information of the person to be predicted, the evaluation step evaluates the health information of the person to be predicted based on the past and hypothetical information from the information necessary for evaluating the health information, and the output step outputs the causal relationship between the information of the person to be predicted, the information necessary for evaluating the health information, and at least two of the health information from the evaluated health information. (Note 20) A health information evaluation support method according to any one of Notes 15 to 19, wherein the evaluation of the health information includes at least one of the prediction of disease onset risk, prediction of health status, and healthcare proposal.(Note 21) A health information evaluation support method according to any one of Notes 15 to 20, wherein the prediction step is a trained model generated by machine learning using information of the person to be predicted, and / or the evaluation step is a trained model generated by machine learning using information necessary for evaluating the health information. (Note 22) A computer-readable recording medium that records a program for causing a computer to execute each of the following steps, the information acquisition step being to acquire information of the person to be predicted, the prediction step being to predict information necessary for evaluating the health information from the information of the person to be predicted, the evaluation step being to evaluate the health information of the person to be predicted from the information necessary for evaluating the health information, and the output step being to output the evaluated health information. (Note 23) The recording medium according to Note 22, wherein the prediction step is to include a plurality of prediction steps, and / or the evaluation step is to include a plurality of evaluation steps. (Note 24) The recording medium according to Note 22 or 23, wherein the information of the person to be predicted includes at least one of the person to be predicted's physical information, examination information, lifestyle information, and medical history, and the information of the person to be predicted is past or hypothetical information. (Note 25) The recording medium according to any one of Notes 22 to 24, wherein the information necessary for evaluating the health information includes at least one of the person to be predicted's predicted physical information and predicted examination information. (Note 26) The recording medium according to any one of Notes 22 to 25, wherein the information of the person to be predicted includes past and hypothetical information, the prediction procedure predicts the information necessary for evaluating the health information based on the past and hypothetical information from the person to be predicted, the evaluation procedure evaluates the health information of the person to be predicted based on the information necessary for evaluating the health information, and the output procedure outputs the causal relationship between the person to be predicted, the information necessary for evaluating the health information, and at least two of the health information from the evaluated health information. (Note 27) A recording medium according to any one of Notes 22 to 26, wherein the evaluation of the health information includes at least one of the following: prediction of disease onset risk, prediction of health status, and suggestion of healthcare.(Note 28) The recording medium according to any one of Notes 22 to 27, wherein the prediction procedure is a trained model generated by machine learning using the information of the person to be predicted, and / or the evaluation procedure is a trained model generated by machine learning using the information necessary for evaluating the health information.

[0050] This application claims priority based on Japanese Patent Application No. 2024-159853, filed on 17 September 2024, and incorporates all of its disclosures herein.

[0051] According to this disclosure, health information can be evaluated even if there are changes in the information necessary for evaluating health information. The fields to which this disclosure can be applied are not limited, and it is useful in a variety of fields using the program described herein.

[0052] 10 Health information evaluation support device 11 Information acquisition unit 12 Prediction unit 13 Evaluation unit 14 Output unit 101 CPU 102 Memory 103 Bus 104 Storage device 105 Input device 106 Output device 107 Communication device

Claims

1. A health information evaluation support program that causes a computer to perform each of the following steps: an information acquisition step, a prediction step, an evaluation step, and an output step, wherein the information acquisition step acquires information of a person to be predicted; the prediction step predicts information necessary for evaluating health information from the information of the person to be predicted; the evaluation step evaluates the health information of the person to be predicted from the information necessary for evaluating health information; and the output step outputs the evaluated health information.

2. The health information evaluation support program according to claim 1, wherein the prediction procedure includes a plurality of prediction procedures, and / or the evaluation procedure includes a plurality of evaluation procedures.

3. The health information evaluation support program according to claim 1 or 2, wherein the information of the person to be predicted includes at least one of the person to be predicted's physical information, examination information, lifestyle information, and medical history, and the information of the person to be predicted is past or hypothetical information.

4. The health information evaluation support program according to claim 1 or 2, which includes at least one of the predicted physical information of the person to be predicted and the predicted examination information as information necessary for evaluating the health information.

5. The health information evaluation support program according to claim 1 or 2, wherein the information of the person to be predicted includes past and hypothetical information, the prediction procedure predicts information necessary for evaluating the health information based on the past and hypothetical information from the information of the person to be predicted, the evaluation procedure evaluates the health information of the person to be predicted based on the past and hypothetical information from the information necessary for evaluating the health information, and the output procedure outputs the causal relationship between the information of the person to be predicted, the information necessary for evaluating the health information, and at least two of the health information from the evaluated health information.

6. The health information evaluation support program according to claim 1 or 2, wherein the evaluation of the health information includes at least one of predicting the risk of disease onset, predicting health status, and proposing healthcare.

7. The health information evaluation support program according to claim 1 or 2, wherein the prediction procedure is a trained model generated by machine learning using information of the person to be predicted, and / or the evaluation procedure is a trained model generated by machine learning using information necessary for evaluating the health information.

8. A health information evaluation support device comprising an information acquisition unit, a prediction unit, an evaluation unit, and an output unit, wherein the information acquisition unit acquires information of a person to be predicted; the prediction unit predicts information necessary for evaluating health information from the information of the person to be predicted; the evaluation unit evaluates the health information of the person to be predicted from the information necessary for evaluating health information; and the output unit outputs the evaluated health information.

9. The health information evaluation support device according to claim 8, wherein the prediction unit includes a plurality of prediction units, and / or the evaluation unit includes a plurality of evaluation units.

10. The health information evaluation support device according to claim 8 or 9, wherein the information of the person to be predicted includes at least one of the person to be predicted's physical information, examination information, lifestyle information, and medical history, and the information of the person to be predicted is past or provisional information.

11. The health information evaluation support device according to claim 8 or 9, wherein the information necessary for evaluating the health information includes at least one of the predicted physical information of the person to be predicted and the predicted examination information.

12. The health information evaluation support device according to claim 8 or 9, wherein the information of the person to be predicted includes past and provisional information, the prediction unit predicts information necessary for evaluating the health information based on the past and provisional information from the information of the person to be predicted, the evaluation unit evaluates the health information of the person to be predicted based on the past and provisional information from the information necessary for evaluating the health information, and the output unit outputs causal relationships between the information of the person to be predicted, the information necessary for evaluating the health information, and at least two of the health information from the evaluated health information.

13. The health information evaluation support device according to claim 8 or 9, wherein the evaluation of the health information includes at least one of predicting the risk of disease onset, predicting health status, and proposing healthcare.

14. The health information evaluation support device according to claim 8 or 9, wherein the prediction unit is a trained model generated by machine learning using information of the person to be predicted, and / or the evaluation unit is a trained model generated by machine learning using information necessary for evaluating the health information.

15. A health information evaluation support method comprising an information acquisition step, a prediction step, an evaluation step, and an output step, wherein the information acquisition step acquires information of a person to be predicted; the prediction step predicts information necessary for evaluating health information from the information of the person to be predicted; the evaluation step evaluates the health information of the person to be predicted from the information necessary for evaluating health information; and the output step outputs the evaluated health information, each of which is performed by a computer.

16. The health information evaluation support method according to claim 15, wherein the prediction step includes a plurality of prediction steps, and / or the evaluation step includes a plurality of evaluation steps.

17. The health information evaluation support method according to claim 15 or 16, wherein the information of the person to be predicted includes at least one of the person to be predicted's physical information, examination information, lifestyle information, and medical history, and the information of the person to be predicted is past or hypothetical information.

18. A method for supporting the evaluation of health information according to claim 15 or 16, wherein the information necessary for evaluating the health information includes at least one of the predicted physical information of the person to be predicted and the predicted examination information.

19. A health information evaluation support method according to claim 15 or 16, wherein the information of the person to be predicted includes past and provisional information, the prediction step predicts information necessary for evaluating the health information based on the past and provisional information from the information of the person to be predicted, the evaluation step evaluates the health information of the person to be predicted based on the past and provisional information from the information necessary for evaluating the health information, and the output step outputs the causal relationship between the information of the person to be predicted, the information necessary for evaluating the health information, and at least two of the health information from the evaluated health information.

20. A computer-readable recording medium that records a program for causing a computer to execute each of the following procedures: an information acquisition procedure, a prediction procedure, an evaluation procedure, and an output procedure, wherein the information acquisition procedure acquires information of a person to be predicted; the prediction procedure predicts information necessary for evaluating health information from the information of the person to be predicted; the evaluation procedure evaluates the health information of the person to be predicted from the information necessary for evaluating health information; and the output procedure outputs the evaluated health information.

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