Disease prediction device, disease prediction method, program, and recording medium

Abstract

The purpose of the present invention is to provide a disease prediction device capable of confirming a disease onset risk even in daily life.　A disease prediction device (10) according to the present invention comprises an information acquisition unit (11), a disease prediction unit (12), and an information output unit (13), wherein: the information acquisition unit (11) acquires past non-self-inspection value information and current self-inspection value information about a disease prediction target person; the disease prediction unit (12) predicts, on the basis of the non-self-inspection value information and the self-inspection value information, a disease that may occur in the future in the disease prediction target person, and generates disease prediction information; and the information output unit (13) outputs the disease prediction information.

Description

Disease prediction device, disease prediction method, program, and recording medium

[0001] The present invention relates to a disease prediction device, a disease prediction method, a program, and a recording medium.

[0002] Health checkups are conducted to understand the health status of individuals, such as determining the risk of developing disease, and the results of the checkups are utilized for health management, such as disease prevention. In addition, a technology for predicting disease based on health checkup information has also been disclosed (Patent Document 1).

[0003] Special Publication No. 2022-551005

[0004] However, while some tests performed during health checkups and comprehensive medical examinations can be performed by individuals themselves, others cannot. Furthermore, health checkups and comprehensive medical examinations are usually conducted once or twice a year, making it difficult to check one's own health condition on a daily basis.

[0005] Therefore, an object of the present invention is to provide a disease prediction device that can check the risk of developing a disease even in everyday life.

[0006] In order to achieve the above object, the disease prediction device of the present invention includes an information acquisition unit, a disease prediction unit, and an information output unit, wherein the information acquisition unit acquires past non-self test value information and current self test value information of a disease prediction subject, the disease prediction unit predicts diseases that may develop in the future in the disease prediction subject based on the non-self test value information and the self test value information, and generates disease prediction information, and the information output unit outputs the disease prediction information.

[0007] The disease prediction method of the present invention includes an information acquisition step, a disease prediction step, and an information output step, wherein the information acquisition step acquires past non-self test value information and current self test value information of a subject for disease prediction, the disease prediction step predicts diseases that may develop in the subject for disease prediction in the future based on the non-self test value information and the self test value information, and generates disease prediction information, and the information output step outputs the disease prediction information, and each of the steps is executed by a computer.

[0008] The program of the present invention includes an information acquisition procedure, a disease prediction procedure, and an information output procedure, wherein the information acquisition procedure acquires past non-self test value information and current self test value information of a disease prediction subject, the disease prediction procedure predicts diseases that may develop in the future in the disease prediction subject based on the non-self test value information and the self test value information, and generates disease prediction information, and the information output procedure outputs the disease prediction information, and the program causes a computer to execute each of the above procedures.

[0009] The recording medium of the present invention includes an information acquisition procedure, a disease prediction procedure, and an information output procedure, wherein the information acquisition procedure acquires past non-self test value information and current self test value information of a disease prediction subject, the disease prediction procedure predicts diseases that may develop in the future in the disease prediction subject based on the non-self test value information and the self test value information, and generates disease prediction information, and the information output procedure outputs the disease prediction information, and the recording medium is computer-readable and stores a program for causing a computer to execute each of the procedures.

[0010] According to the present invention, it becomes possible to check the risk of developing a disease even in daily life, and it is possible to raise awareness of individual health management.

[0011] FIG. 1 is a block diagram showing an example of the configuration of a disease prediction device of embodiment 1. FIG. 2 is a block diagram showing an example of the hardware configuration of the disease prediction device of embodiment 1. FIG. 3 is a flowchart showing an example of processing in the disease prediction device of embodiment 1. FIG. 4 is a block diagram showing an example of the configuration of an example of a disease prediction device of embodiment 2. FIG. 5 is a flowchart showing an example of processing in the disease prediction device of embodiment 2. FIG. 6 is a reference diagram explaining an example of estimation of estimated test value information in the disease prediction device of embodiment 2. FIG. 7 is a reference diagram explaining an example of a disease prediction procedure in the disease prediction device of embodiment 2.

[0012] Embodiments of the present invention will be described with reference to the drawings. The present invention 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] [Embodiment 1] Fig. 1 is a block diagram showing an example of the configuration of a disease prediction device 10A (hereinafter also referred to as "the device 10A") according to this embodiment. As shown in Fig. 1, the device 10A includes an information acquisition unit 11, a disease prediction unit 12, and an information output unit 13. Furthermore, although not shown, the device 10A may also include, for example, an input unit, an output unit, a display unit, and / or a storage unit.

[0014] The device 10A 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 10A can also be connected to an external device (described later) via the communication network. The communication network is not particularly limited and any known network can be used, and may be 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 networks include Wi-Fi (registered trademark), Bluetooth (registered trademark), Local 5G, and a 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 10A may be incorporated into, for example, a server of a system. The device 10A may also be, for example, a personal computer (PC, for example, a desktop or notebook type) on which the program of the present invention is installed, a smartphone, a tablet terminal, or the like. Furthermore, the device 10A may also be in the form of cloud computing or edge computing, for example, in which at least one of the units is located on a server and the other units are located on terminals.

[0015] 2 is a block diagram illustrating the hardware configuration of the device 10A. The device 10A 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 10A 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 is responsible for overall control of the device 10A. In the device 10A, the central processing unit 101 executes, for example, the program of the present invention and other programs, and reads and writes various information. Specifically, for example, the central processing unit 101 functions as an information acquisition unit 11, a disease prediction unit 12, and an information output unit 13. If the device 10A includes the output unit, the central processing unit 101 may function as the output unit. The device 10A may include, as a computing device, other computing devices such as a CPU, a GPU (Graphics Processing Unit), or an APU (Accelerated Processing Unit), or may include a combination of a CPU and 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 10A 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 invention, 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, in contrast to the main memory (primary storage device). As described above, the storage device 104 stores operating programs, including the program of the present invention. The storage device 104 may be, for example, a combination of a recording medium and a drive for reading and writing data from and to the recording medium. The recording medium is not particularly limited and may be internal or external, and examples include a hard disk drive (HDD), 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) that integrates a recording medium and a drive. If the device 10A includes the storage unit, for example, the storage device 104 functions as the storage unit. The storage unit can store, for example, past non-self-test value information and current self-test value information, as described below.

[0020] In the present device 10A, the memory 102 and the storage device 104 can also store various information such as log information, information obtained from an external database (not shown) or an external device, information generated by the present device 10A, and information used when the present device 10A executes processing. In this case, the memory 102 and the storage device 104 may store, for example, the past non-self-test value information. 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 10A 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, and mouse; a keyboard; imaging means such as a camera and scanner; card readers such as an IC card reader and 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 and a liquid crystal display; audio output devices such as a speaker; and a printer. In the first embodiment, 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 disease prediction method of this embodiment will be described based on the flowchart of Fig. 3. The disease prediction method of this embodiment is carried out as follows, for example, using the present device 10A of Fig. 1 or Fig. 2. Note that the disease prediction method of this embodiment is not limited to use with the present device 10A of Fig. 1 or Fig. 2.

[0023] First, the information acquisition unit 11 acquires past non-self test value information and current self test value information for the disease prediction subject (S1, information acquisition step). The self test value information is information on test values ​​measured by the disease prediction subject himself / herself for test items that the disease prediction subject can measure on a daily basis, such as weight, blood pressure, BMI, and self-examination. The self test value information may also include attribute information on the disease prediction subject, such as gender and age. The non-self test value information is information on test values ​​other than the self test value information, such as test values ​​obtained through health checkups or the like that include test items that the disease prediction subject cannot measure themselves, such as blood test values ​​and urine test values. The non-self test value information is not limited to test values ​​obtained through health checkups, but may also include information related to test results from comprehensive medical checkups, individual diagnostic results, etc. Furthermore, the non-self test value information is not limited to actual test values ​​from the past, but may also be test values ​​estimated from, for example, test value trends, the passage of time, correlations with self test value information, etc. The past non-self-test value information and current self-test value information can be obtained, for example, by obtaining them from an external database or by directly inputting them into an input device, but this is not limited to these methods and any method that can obtain the above-mentioned information can be used, such as receiving them from an external terminal via a communication line.

[0024] Next, the disease prediction unit 12 predicts diseases that the disease prediction subject may develop in the future based on the non-self test value information and the self test value information, generating disease prediction information (S2, disease prediction process). Predictions of the likelihood of disease development are typically performed using non-self test values ​​from health checkups, etc., which typically occur once or twice a year. Therefore, to predict the likelihood of disease development in everyday life outside of health checkups, etc., the disease prediction unit 12 uses the daily self test value information measured by the disease prediction subject himself / herself in addition to the non-self test value information. Potential future diseases are predicted based on the non-self test value information and the self test value information, for example, from the correlation between each test value and the disease. Disease prediction may use, for example, a disease development prediction model. The disease prediction information is not limited to the likelihood of disease development and may also include predictions of the health status of the disease prediction subject.

[0025] Next, the information output unit 13 outputs the disease prediction information (S3, information output step). The output of the disease prediction information is displayed, for example, on a dedicated monitor connected as an external device. Note that the output is not limited to this, and may be, for example, transmitted to a terminal of the subject of disease prediction via a communication line, or output to an external database, and may be set arbitrarily according to the intended use.

[0026] The device 10A of this embodiment can predict diseases that may occur in the future based on non-self-test value information and self-test value information. Therefore, by using the device 10A of this embodiment, disease prediction can be performed in everyday life, regardless of the timing of a health checkup, for example. This can increase individual awareness of health management.

[0027] [Embodiment 2] Embodiment 2 is another example of a disease prediction device of the present invention.

[0028] The disease prediction device of this embodiment will be described with reference to FIG. 4. FIG. 4 is a block diagram showing an example of the configuration of a disease prediction device 10B of this embodiment (hereinafter also referred to as "the device 10B"). As shown in FIG. 4, the device 10B of this embodiment includes the same components as the device 10A of embodiment 1 (information acquisition unit 11, disease prediction unit 12, and information output unit 13), except that the disease prediction unit 12 includes, for example, a test value estimation unit 14. In the device 10B, the configuration, including the hardware configuration, is the same as that of the device 10A of embodiment 1, and the description therefor can be used.

[0029] Next, an example of the disease prediction method of this embodiment will be described with reference to the flowchart of Fig. 5. The disease prediction method of this embodiment is carried out as follows, for example, using the present device 10B shown in Fig. 4. Note that the disease prediction method of this embodiment is not limited to use of the present device 10B shown in Fig. 4.

[0030] The information acquisition unit 11 of the device 10B of this embodiment acquires past non-self test value information and current self test value information for the disease prediction subject (S1B, information acquisition step). The information acquisition step is the same as in the first embodiment, and therefore the description thereof can be used.

[0031] Next, the test value estimation unit 14 included in the disease prediction unit 12 estimates current test values ​​based on at least one of the past non-self test information and the self test value information to generate estimated test value information (S2B, test value estimation step). As mentioned above, people typically undergo health checkups, etc., once or twice a year. Therefore, when disease prediction is performed at a time other than a health checkup, etc., the non-self test information is from before the disease prediction. Therefore, estimating current test values ​​from the non-self test information enables more accurate disease prediction. The test value estimation unit 14 estimates current test values, for example, from the trends in the past non-self test information. Furthermore, the test value estimation may be performed, for example, based on the correlation between the past non-self test information and the self test value information. The past non-self test information used in the correlation analysis may be past test values ​​directly or estimated values. The test value estimation is not limited to the above examples and may be performed using at least one of the past non-self test information and the self test value information.

[0032] As an example of estimating the test value, the test value estimating unit 14 may generate estimated test value information by generating an approximation line that predicts fluctuations in non-self test values ​​from multiple pieces of past non-self test value information, and estimating a current test value based on the approximation line. The approximation line represents a predicted value based on multiple past test values, and may be, for example, an approximation curve, an approximation straight line, an approximation broken line, etc.

[0033] As another example, the generation of estimated test value information by the test value estimation unit 14 may be performed by estimating the current test value based on the correlation between the non-self test value information and the self test value information.

[0034] Next, the disease prediction unit 12 generates disease prediction information by predicting diseases that may develop in the disease prediction subject in the future, based on the estimated laboratory value information and self-examination value information generated by the laboratory value estimation unit 14 (S3B, disease prediction step). The disease prediction is the same as in the first embodiment, except that the estimated laboratory value information generated by the laboratory value estimation unit 14 is used, and therefore the description therefor can be cited.

[0035] Next, the information output unit 13 outputs the disease prediction information (S4B, information output step). The information output step is the same as in the first embodiment, and therefore the description therefor can be used.

[0036] An example of a specific process for disease prediction using the device 10B will be described below. Note that the following description is an example of a specific flow, and as described above, the device 10B and the disease prediction method of this embodiment are not limited to the following description, and the disease prediction method of this embodiment is not limited to use of the device 10B.

[0037] As described above, the possibility of disease onset can be predicted based on test values ​​measured in a health checkup or the like. Furthermore, in making the prediction, test values ​​that the disease prediction subject can measure themselves can be replaced with test values ​​from a health checkup or the like, and test values ​​from a health checkup or the like can also be replaced with current estimated values. Examples of test items required for predicting the possibility of disease onset include basic information (age, gender, etc.), blood test results (LDL, HDL, etc.), medical interview information (smoking, diet, etc.), and items that can be measured daily (blood pressure, weight, BMI, etc.). All of these items are confirmed, tested, and measured in a health checkup or the like, and the only item that is difficult for the disease prediction subject to measure themselves is the blood test. Therefore, if the current test values ​​of a blood test can be estimated using other test values, the possibility of disease onset can be predicted in the same way as when a health checkup or the like is performed.

[0038] When the trend in blood test value fluctuations can be understood based on multiple past blood test values, an approximate curve can be generated to estimate current test values. FIG. 6 is an illustration of an approximate curve generated from past test values. The black circles in FIG. 6 represent past actual values. The horizontal axis of the table represents the passage of time, allowing the actual values ​​over time to be understood. The curve in FIG. 6 is an approximate curve generated based on past actual values. Using this approximate curve, the rate of change in test values ​​can be understood based on the time elapsed since the previous health checkup, etc. The double circles in FIG. 6 represent the corresponding portion of the approximate curve based on the time elapsed since the previous health checkup. This makes it possible to estimate current test values ​​using test values ​​from the previous health checkup. Note that estimation of current test values ​​based on multiple past non-self test value information is not limited to blood tests, and the approximate line used in the analysis is not limited to an approximate curve. In this way, the test value estimation unit 14 generates an approximate line predicting fluctuations in non-self test values ​​from multiple past non-self test value information, and estimates and generates current test values ​​based on the approximate line.

[0039] In addition, the correlation between test values ​​that the disease prediction subject can measure themselves and the test values ​​of the blood test is analyzed. For example, age, gender, changes in lifestyle, blood pressure status, weight gain or loss, etc. are thought to affect the test values ​​of the blood test. The current test values ​​can be estimated based on, for example, statistical trends and correlations between test values ​​measured by the disease prediction subject themselves, such as age. Note that the test values ​​of the blood test may be past test values ​​as they are, or estimated values ​​may be used. As mentioned above, the estimation of the current test values ​​is not limited to blood tests.

[0040] As described above, for test items for which the subject does not have a test value measured by themselves as current self-test value information, the current test value is estimated based on at least one of past non-self-test information and current self-test value information, and the likelihood of disease onset is predicted based on the estimated test value information and the current self-test value information. Figure 7 shows an example of test values ​​used in the prediction. In Figure 7, the passage of time is represented to the right, indicating whether each test item is an actual measured value or an estimated value. The prediction of the likelihood of disease onset at the time shown in the upper row is made based on the test values ​​shown in the corresponding column.

[0041] The device 10B of this embodiment estimates current information for test items for which the subject of disease prediction has not measured test values ​​themselves, making it possible to make predictions based on information that better captures the current situation.

[0042] [Embodiment 3] The program of this embodiment is a program for causing a computer to execute each step of the disease prediction method described above. Specifically, the program of this embodiment is a program for causing a computer to execute, for example, an information acquisition procedure, a disease prediction procedure, and an information output procedure.

[0043] The information acquisition procedure acquires past non-self test value information and current self test value information for the disease prediction subject; the disease prediction procedure predicts diseases that may develop in the future for the disease prediction subject based on the non-self test value information and the self test value information, and generates disease prediction information; and the information output procedure outputs the disease prediction information.

[0044] The program of this embodiment can also be said to be a program that causes a computer to function as, for example, an information acquisition procedure, a disease prediction procedure, and an information output procedure.

[0045] The program of this embodiment can be implemented by incorporating the descriptions of the disease prediction device and disease prediction method of the present invention. For example, the term "procedure" in each of the steps can be replaced with "processing." The program of this embodiment may be recorded on a computer-readable recording medium, for example. The recording medium may be 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., SSD (Solid State Drive), USB flash memory, SD / SDHC card, etc.), optical disk (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 this embodiment (also referred to as a programming product or 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 this embodiment may be installed and executed on the device to which it is distributed, or may be executed without being installed.

[0046] Although the present invention has been described above with reference to the embodiments, the present invention 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 invention within the scope of the present invention.

[0047] <Supplementary Notes> Some or all of the above embodiments may be described as, but are not limited to, the following supplementary notes. (Supplementary Note 1) A disease prediction device comprising an information acquisition unit, a disease prediction unit, and an information output unit, wherein the information acquisition unit acquires past non-self test value information and current self test value information of a disease prediction subject, wherein the disease prediction unit predicts a disease that may develop in the future in the disease prediction subject based on the non-self test value information and the self test value information, and generates disease prediction information, and the information output unit outputs the disease prediction information. (Supplementary Note 2) The disease prediction device according to Supplementary Note 1 further comprises a test value estimation unit, wherein the test value estimation unit estimates current test values ​​based on at least one of the past non-self test information and the self test value information, and generates estimated test value information, and wherein the disease prediction unit predicts a disease that may develop in the future in the disease prediction subject based on the estimated test value information and the self test value information. (Supplementary Note 3) The disease prediction device according to Supplementary Note 2, wherein the test value estimation unit generates an approximation line predicting fluctuations in non-self test values ​​from a plurality of pieces of past non-self test value information, and estimates current test values ​​based on the approximation line to generate estimated test value information. (Supplementary Note 4) The disease prediction device according to Supplementary Note 2, wherein the test value estimation unit estimates current test values ​​based on a correlation between the non-self test value information and the self test value information to generate estimated test value information. (Supplementary Note 5) A disease prediction method comprising an information acquisition step, a disease prediction step, and an information output step, wherein the information acquisition step acquires past non-self test value information and current self test value information for a subject for disease prediction, the disease prediction step predicts diseases that may develop in the subject in the future based on the non-self test value information and the self test value information to generate disease prediction information, and the information output step outputs the disease prediction information, and each of the steps is executed by a computer.(Supplementary Note 6) The disease prediction method of Supplementary Note 5, wherein the disease prediction step further includes a test value estimating step of estimating current test values ​​based on at least one of the past non-self test value information and the past self test value information to generate estimated test value information, and the disease prediction step of predicting diseases that may develop in the disease prediction subject based on the estimated test value information and the past self test value information to generate disease prediction information, and each of the steps is performed by a computer. (Supplementary Note 7) The disease prediction method of Supplementary Note 6, wherein the test value estimating step generates an approximation line predicting fluctuations in non-self test values ​​from multiple pieces of past non-self test value information, and estimates current test values ​​based on the approximation line to generate estimated test value information, and each of the steps is performed by a computer. (Supplementary Note 8) The disease prediction method of Supplementary Note 6, wherein the test value estimating step estimates current test values ​​based on a correlation between the non-self test value information and the past self test value information to generate estimated test value information, and each of the steps is performed by a computer. (Supplementary Note 9) A program for causing a computer to execute each of the steps, the program comprising: an information acquisition step, a disease prediction step, and an information output step, wherein the information acquisition step acquires past non-self test value information and current self test value information of a subject for disease prediction, the disease prediction step predicts a disease that may develop in the subject in the future based on the non-self test value information and the self test value information, and generates disease prediction information, and the information output step outputs the disease prediction information. (Supplementary Note 10) The program according to Supplementary Note 9 for causing a computer to execute each of the steps, the disease prediction step further comprising a laboratory value estimation step, wherein the laboratory value estimation step estimates a current laboratory value based on at least one of the past non-self test information and the self test value information, and generates estimated laboratory value information, and the disease prediction step predicts a disease that may develop in the subject in the future based on the estimated laboratory value information and the self test value information.(Supplementary Note 11) The program according to Supplementary Note 10, for causing a computer to execute each of the steps, wherein the test value estimation step generates an approximation line predicting fluctuations in non-self test values ​​from a plurality of pieces of past non-self test value information, and estimates current test values ​​based on the approximation line to generate estimated test value information. (Supplementary Note 12) The program according to Supplementary Note 10, for causing a computer to execute each of the steps, wherein the test value estimation step estimates current test values ​​based on a correlation between the non-self test value information and the self test value information to generate estimated test value information. (Supplementary Note 13) A computer-readable recording medium having recorded thereon a program for causing a computer to execute each of the steps, the program including an information acquisition step, a disease prediction step, and an information output step, wherein the information acquisition step acquires past non-self test value information and current self test value information for a disease prediction subject, the disease prediction step predicts a disease that may develop in the disease prediction subject based on the non-self test value information and the self test value information to generate disease prediction information, and the information output step outputs the disease prediction information. (Supplementary Note 14) The computer-readable recording medium of Supplementary Note 13, having recorded thereon a program for causing a computer to execute each of the above steps, wherein the disease prediction procedure further includes a test value estimation procedure, wherein the test value estimation procedure estimates current test values ​​based on at least one of the past non-self test information and the past self test value information to generate estimated test value information, and the disease prediction procedure predicts diseases that may develop in the disease prediction subject based on the estimated test value information and the past self test value information to generate disease prediction information. (Supplementary Note 15) The computer-readable recording medium of Supplementary Note 14, having recorded thereon a program for causing a computer to execute each of the above steps, wherein the test value estimation procedure generates an approximation line that predicts fluctuations in non-self test values ​​from multiple pieces of past non-self test value information, and estimates current test values ​​based on the approximation line to generate estimated test value information.(Supplementary Note 16) The test value estimation procedure estimates a current test value based on a correlation between the non-self test value information and the self test value information to generate estimated test value information, and the computer-readable recording medium described in Supplementary Note 14 stores a program for causing a computer to execute each of the procedures.

[0048] This application claims priority based on Japanese Patent Application No. 2023-060749, filed on April 4, 2023, the disclosure of which is incorporated herein in its entirety by reference.

[0049] According to the present invention, it becomes possible to check the risk of developing a disease even in daily life, and it is possible to raise awareness of individual health management.

[0050] 10A, 10B Disease prediction device 11 Information acquisition unit 12 Disease prediction unit 13 Information output unit 14 Test value estimation unit 101 Central processing unit 102 Memory 103 Bus 104 Storage device 105 Input device 106 Output device 107 Communication device

Claims

1. An information acquisition unit, a disease prediction unit, and an information output unit, the information acquisition unit acquires past non-self-examination value information and current self-examination value information of the disease prediction subject, the disease prediction unit predicts a disease that may develop in the future in the disease prediction subject based on the non-self test value information and the self test value information, and generates disease prediction information; The information output unit outputs the disease prediction information. Disease prediction device.

2. The disease prediction unit further includes a test value estimation unit, the test value estimation unit estimates current test values ​​based on at least one of the past non-self test value information and the past self test value information to generate estimated test value information; the disease prediction unit predicts a disease that may develop in the future in the disease prediction subject based on the estimated test value information and the self-examination value information, and generates disease prediction information. The disease prediction device according to claim 1.

3. the test value estimation unit generates an approximation line predicting fluctuations in the non-self test values ​​from a plurality of pieces of past non-self test value information, and estimates current test values ​​based on the approximation line to generate estimated test value information. The disease prediction device according to claim 2.

4. the test value estimation unit estimates a current test value based on a correlation between the non-self test value information and the self test value information to generate estimated test value information. The disease prediction device according to claim 2.

5. The method includes an information acquisition step, a disease prediction step, and an information output step, The information acquisition step acquires past non-self-examination value information and current self-examination value information of the disease prediction subject, the disease prediction step predicts a disease that may develop in the future in the disease prediction subject based on the non-self test value information and the self test value information, and generates disease prediction information; The information output step outputs the disease prediction information, Each of the steps is executed by a computer. Disease prediction methods.

6. The disease prediction step further includes a test value estimation step, the test value estimating step includes estimating current test values ​​based on at least one of the past non-self test value information and the past self test value information to generate estimated test value information; the disease prediction step predicts a disease that may develop in the future in the disease prediction subject based on the estimated test value information and the self-examination value information, and generates disease prediction information; Each of the steps is executed by a computer. The disease prediction method according to claim 5.

7. the test value estimating step generates an approximation line predicting fluctuations in the non-self test values ​​from a plurality of pieces of past non-self test value information, and estimates current test values ​​based on the approximation line to generate estimated test value information; Each of the steps is executed by a computer. The disease prediction method according to claim 6.

8. the test value estimating step estimates a current test value based on a correlation between the non-self test value information and the self test value information to generate estimated test value information; Each of the steps is executed by a computer. The disease prediction method according to claim 6.

9. The method includes an information acquisition step, a disease prediction step, and an information output step, The information acquisition step acquires past non-self-examination value information and current self-examination value information of the disease prediction subject, the disease prediction step predicts a disease that may develop in the disease prediction subject in the future based on the non-self test value information and the self test value information, and generates disease prediction information; the information output step outputs the disease prediction information; A program for causing a computer to execute each of the above procedures.

10. The method includes an information acquisition step, a disease prediction step, and an information output step, The information acquisition step acquires past non-self-examination value information and current self-examination value information of the disease prediction subject, the disease prediction step predicts a disease that may develop in the disease prediction subject in the future based on the non-self test value information and the self test value information, and generates disease prediction information; the information output step outputs the disease prediction information; A computer-readable recording medium that records a program for causing a computer to execute each of the above procedures.