Explanation information provision program, explanation information provision device, explanation information provision method, and recording medium

Abstract

This disclosure aims to provide an explanatory information program that can offer explanations that take into account the relationships between diseases. [Solution] The explanatory information provision program of this disclosure includes a prediction procedure, a generation procedure, and an output procedure, wherein the prediction procedure generates disease risk information for multiple diseases based on biological information, the generation procedure generates explanatory information for the target disease based on a combination of the disease risk information, and the output procedure outputs the explanatory information.

Description

Technical Field

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[0001] The present disclosure relates to an explanatory information providing program, an explanatory information providing apparatus, an explanatory information providing method, and a recording medium.

Background Art

[0002] Patent Document 1 discloses a disease risk assessment apparatus including: a disease level prediction unit that takes user's diagnostic data as input, predicts a disease level by a learned model, and outputs a disease level prediction score; the learned model; and a prediction reason analysis unit that analyzes the reason for the disease level prediction indicated by the disease level prediction score using the learned model and the diagnostic data input to the learned model, and outputs the reason for the disease level prediction, and a disease countermeasure output unit that outputs a disease countermeasure message based on the reason for the disease level prediction output from the prediction reason analysis unit.

Prior Art Documents

Patent Documents

[0003] [[ID=​​​​​​​​​​​​​​​​​​​​​​​To achieve the aforementioned objectives, the explanatory information program of this disclosure is: Includes prediction procedure, generation procedure, and output procedure, The aforementioned prediction procedure generates disease risk information for multiple diseases based on biological information, with a predicted risk of onset for each disease. The above generation procedure generates explanatory information for the target disease based on the combination of the disease risk information, The output procedure outputs the explanatory information. This is a program that causes a computer to execute each of the aforementioned steps.

[0007] The explanatory information provider in this disclosure is Including a prediction unit, a generation unit, and an output unit, The prediction unit generates disease risk information for multiple diseases based on biological information, relating to the predicted risk of onset for each disease. The generation unit generates explanatory information about the target disease based on the combination of the disease risk information. The output unit outputs the explanatory information. It is a device.

[0008] The method of providing explanatory information in this disclosure is: Including a prediction process, a generation process, and an output process, The aforementioned prediction step generates disease risk information for multiple diseases based on biological information, relating to the predicted risk of onset for each disease. The generation process generates explanatory information about the target disease based on the combination of disease risk information. The output step outputs the explanatory information. This method involves each of the aforementioned steps being performed by a computer.

[0009] The recording medium disclosed herein is Includes prediction procedure, generation procedure, and output procedure, The aforementioned prediction procedure generates disease risk information for multiple diseases based on biological information, with a predicted risk of onset for each disease. The above generation procedure generates explanatory information for the target disease based on the combination of the disease risk information, The output procedure outputs the explanatory information. This is a computer-readable recording medium that contains a program providing explanatory information to cause a computer to perform each of the aforementioned procedures. [Effects of the Invention]

[0010] This disclosure allows us to provide explanations that take into account the relationships between diseases. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 is a block diagram showing an example of the configuration of the explanatory information providing device of this disclosure. [Figure 2] Figure 2 is a block diagram showing an example of the hardware configuration of the explanatory information provider device of this disclosure. [Figure 3] Figure 3 is a flowchart illustrating an example of the procedure provided by the explanatory information program described herein. [Figure 4] Figure 4 is a schematic diagram showing an example of correspondence information in the explanatory information provision program of this disclosure. [Modes for carrying out the invention]

[0012] The embodiments of this disclosure will be described below with reference to the drawings. This disclosure is not limited to the embodiments described below. In the following drawings, the same parts are denoted by the same reference numerals. Unless otherwise specified, the descriptions of each embodiment can be used interchangeably, and the configurations of each embodiment can be combined unless otherwise specified. In this disclosure, each drawing may correspond to one or more embodiments.

[0013] [Embodiment 1] The explanatory information providing program of the present disclosure is a program for causing a computer to execute a prediction procedure, a generation procedure, and an output procedure. The explanatory information providing program of the present disclosure can also be said to be a program that causes a computer to function as a prediction procedure, a generation procedure, and an output procedure. Further, the explanatory information providing program of the present disclosure can also be said to be a program for causing a computer to execute each step of the explanatory information providing method described later, for example.

[0014] The prediction procedure generates risk-of-onset information regarding the risk of onset predicted for each of a plurality of diseases based on biological information. The generation procedure generates explanatory information for a target disease based on a combination of the risk-of-onset information. The output procedure outputs the explanatory information.

[0015] Each of the above procedures can be read as "processing" instead of "procedure", for example. Also, the explanatory information providing program of the present disclosure may be recorded on a computer-readable recording medium, for example. The recording medium is, for example, a non-transitory computer-readable storage medium. The recording medium is not particularly limited, and examples include random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., solid state drive (SSD), USB flash memory, SD / SDHC card, etc.), optical disk (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disk (MO), floppy (registered trademark) disk (FD), etc. Further, the explanatory information providing program of the present disclosure (also referred to as a programming product or a program product, for example) may be in a form distributed from an external computer, for example. The "distribution" may be, for example, distribution via a communication network or distribution via a wired-connected device. The explanatory information providing program of the present disclosure may be installed and executed on the distributed device, or may be executed without installation. The information processing device capable of executing the explanatory information providing program of the present disclosure can be referred to as the explanatory information providing device of the present disclosure, for example.

[0016] Next, an example of the configuration of the explanatory information providing device of the present disclosure will be described using FIG. 1. FIG. 1 is a block diagram showing an example of the configuration of the explanatory information providing device 10 (hereinafter also referred to as the present device 10) of the present disclosure. As shown in FIG. 1, the present device 10 includes a prediction unit 11, a generation unit 12, and an output unit 13. Further, although not shown, the present device 10 may include, for example, an input unit, other output units, a display unit, and / or a storage unit. The prediction unit 11, the generation unit 12, and the output unit 13 can each execute, for example, the prediction procedure, the generation procedure, and the output procedure in the explanatory information providing program of the present disclosure.

[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 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 the Internet, WWW (World Wide Web), telephone lines, 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 (registered trademark), Bluetooth (registered trademark), 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, for example, incorporated into a server as a system. Furthermore, the device 10 may be, for example, a personal computer (PC, e.g., desktop or notebook), smartphone, tablet terminal, etc., on which the program of this disclosure is installed. The device 10 may also be in the form of cloud computing or edge computing, for example, in which at least one of the aforementioned parts is on a server and the other aforementioned 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 (explanatory information provision program) and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 101 functions as a prediction unit 11, a generation unit 12, and an output unit 13. The device 10 may also include other computing devices such as a CPU, GPU (Graphics Processing Unit), APU (Accelerated Processing Unit), or a combination thereof as computing devices.

[0020] Bus 103 can also be connected to external devices, for example. Examples of such external devices include external storage devices (external databases, etc.), 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) via 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). If the device 10 includes the storage unit, for example, the storage device 104 functions as the storage unit. The storage unit can record, for example, biological information, disease risk, disease risk information, explanatory information, biological state information, etc., which will be described later.

[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, the user information of this device as described above. 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 embodiment 1, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may be configured as an integrated unit, such as a touch panel display.

[0025] An example of processing by the explanatory information program described herein will be explained in more detail using Figure 3. Figure 3 is a flowchart showing an example of each step in the explanatory information program described herein.

[0026] The prediction unit 11 generates disease risk information for multiple diseases based on biological information, relating to the predicted risk of onset for each disease (S1, prediction procedure). The prediction unit 11 may include, for example, a disease risk prediction unit that predicts the risk of onset and a disease risk information generation unit that generates the disease risk information. In this case, the prediction procedure may include, for example, a disease risk prediction procedure and a disease risk information generation procedure.

[0027] The aforementioned biological information is information that reflects the state of a living organism. The living organism may be, for example, a human or an animal other than a human. The aforementioned biological information may also be information obtained through, for example, a health checkup, a medical examination, or a healthcare service. The healthcare service may be, for example, Fornes Life Co., Ltd.'s Fornes Visual® examination. The number of the aforementioned biological information may be, for example, one or two or more. The type of the aforementioned biological information may be, for example, one or two or more. The aforementioned biological information may be, for example, quantitative or qualitative data of biological components, quantitative or qualitative data of physiological functions, or quantitative or qualitative data of biological structures. The aforementioned biological components may be, for example, proteins, carbohydrates, lipids, nucleic acids, inorganic substances, organic substances, or cells. The aforementioned physiological functions may be, for example, those examined by physiological function tests. Examples of the physiological function tests include electrocardiograms, respiratory function tests, electroencephalograms, magnetoencephalograms, electromyograms, pulse wave tests, blood pressure tests, fundus examinations, body temperature tests, visual acuity tests, and hearing tests. Examples of the devices used to perform the physiological function tests include medical devices and wearable devices. Examples of the biological structures used include those examined by imaging tests and those examined by pathological tests. Examples of the imaging tests include ultrasound, X-ray, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and nuclear medicine tests. Examples of the pathological tests include cytological tests, histopathological tests, and immunohistochemical tests. Examples of the quantitative data include the quantitative data itself, feature data calculated from the quantitative data by a known method, and integrated data calculated from a combination of multiple quantitative data by a known method. Examples of the feature data include the mean, mode, median, standard deviation, and coefficient of variation. Examples of the integrated data include data calculated from combinations of quantitative data of the biological components, data calculated from combinations of quantitative data of the physiological functions, and data calculated from quantitative data of the biological structure.Furthermore, the integrated data may include, for example, data calculated from a combination of at least two of the quantitative data of the biological components, the quantitative data of the physiological functions, and the quantitative data of the biological structure.

[0028] The biometric information may include, for example, other information. Examples of this other information include biometric identification information that can identify the organism, gender information indicating the organism's sex, physical attribute information indicating the organism's physical attributes, medical history information indicating the organism's medical history, lifestyle information indicating the organism's lifestyle habits, and burden information indicating the organism's physical and mental burden. Examples of biometric identification information include identification numbers. Examples of gender information include male, female, etc. Examples of physical attribute information include age, height, weight, BMI (Body Mass Index), waist circumference, blood type, etc. Examples of medical history information include the organism's medical history, the medical history of organisms related to the organism by blood, etc. Examples of lifestyle information include eating habits, snacking habits, drinking habits, smoking habits, sleeping habits, exercise habits, medication habits, etc. Examples of burden information include fatigue levels, stress levels, work conditions, etc.

[0029] The disease is, for example, a condition in which the organism is impaired. Examples of the disease include infectious diseases, parasitic diseases, neoplasms, diseases of the blood and hematopoietic system, disorders of the immune system, endocrine diseases, nutritional diseases, metabolic diseases, mental and behavioral disorders, diseases of the nervous system, diseases of the eyes and adnexa, diseases of the ears and mastoid process, diseases of the circulatory system, diseases of the respiratory system, diseases of the digestive system, diseases of the skin and subcutaneous tissue, diseases of the musculoskeletal system and connective tissue, diseases of the renal, urinary, and reproductive systems, conditions occurring during pregnancy, childbirth, the postpartum period, and the perinatal period, congenital malformations, deformities, chromosomal abnormalities, injuries, and poisoning. Examples of the neoplasm include malignant neoplasms and carcinoma in situ. Malignant neoplasms may be, for example, primary, secondary, of unclear site, or of unknown site. The site of the malignant neoplasm may be, for example, the lips, oral cavity and pharynx, digestive organs, respiratory organs and intrathoracic organs, bones and articular cartilage, skin, mesothelium and soft tissues, breasts, female reproductive organs, male reproductive organs, kidneys and urinary tract, eyes, brain and other parts of the central nervous system, thyroid gland and other endocrine glands, lymphoid tissue, hematopoietic tissue and related tissues. The malignant neoplasm may be, for example, lung cancer, breast cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, prostate cancer, etc. The mental and behavioral disorder may be, for example, dementia, etc. The cardiovascular disease may be, for example, stroke, cerebral infarction, myocardial infarction, heart failure, etc. The disease of the renal, urinary, and reproductive system may be, for example, chronic renal failure, etc.

[0030] The aforementioned disease onset risk is, for example, the risk of developing the disease within a predetermined period. The predetermined period may be, for example, 4 years, 5 years, 20 years, etc. The aforementioned disease onset risk may be, for example, risk, odds, risk ratio, odds ratio, etc. The aforementioned disease onset risk may be predicted by, for example, a known method. The known method may be, for example, a regression equation relating to the disease onset risk for each disease, or a disease onset risk prediction model relating to the disease onset risk for each disease. The regression equation may be, for example, a simple regression equation or a multiple regression equation. The regression equation may be, for example, generated by multivariate regression analysis such as logistic regression analysis. The disease onset risk prediction model may be, for example, generated by machine learning.

[0031] The prediction unit 11 may predict the disease onset risk using, for example, a known regression equation. The regression equation is, for example, an equation that takes the biological information as an explanatory variable and outputs the disease onset risk as the dependent variable. The regression equation may be, for example, an equation that outputs the disease onset risk for one or more of the multiple diseases, or an equation that outputs the disease onset risk for each of the multiple diseases. In the former case, the prediction unit 11 may, for example, use multiple regression equations to predict the disease onset risk for each of the multiple diseases. The regression equation may be appropriately selected, for example, depending on the multiple diseases to be predicted.

[0032] The prediction unit 11 may predict the disease risk using, for example, a known disease risk prediction model. The disease risk prediction model is, for example, a model that predicts the disease risk by inputting the biological information. The disease risk prediction model is, for example, a trained model generated by machine learning using the biological information as training data to predict the disease risk when the biological information is input. The disease risk prediction model may, for example, be a model that predicts the disease risk for one or more of the multiple diseases, or it may be a model that predicts the disease risk for each of the multiple diseases. In the former case, the prediction unit 11 may, for example, use multiple disease risk prediction models to predict the disease risk for each of the multiple diseases. The disease risk prediction model may be appropriately selected, for example, depending on the multiple diseases to be predicted.

[0033] The regression equation may be stored, for example, in the memory 102 or storage device 104 of the device of this disclosure, or in the memory or storage device of a device other than the device of this disclosure. The disease risk prediction model may also be stored, for example, in the memory 102 or storage device 104 of the device of this disclosure, or in the memory or storage device of a device other than the device of this disclosure.

[0034] The disease risk information is information relating to the predicted disease risk for each of the multiple diseases. Examples of the disease risk information include the disease risk itself, converted disease risk information obtained by converting the disease risk into a different representation format, and categorized disease risk information obtained by dividing the disease risk into multiple categories.

[0035] The converted disease risk information is, for example, information obtained by converting the disease risk into a different representation format. Examples of these different representation formats include a format in which the disease risk is expressed as a disease incidence rate compared to a healthy population, a format in which the disease risk is expressed as a disease incidence probability compared to a healthy population, and a format in which the disease risk is expressed as the number of people who develop the disease per predetermined number of people. An example of the representation format for the disease incidence rate is "the disease risk is X times." An example of the representation format for the disease incidence probability is "the disease risk is X%." An example of the representation format for the number of people who develop the disease per predetermined number of people is "X people develop the disease per 100 people." The predetermined number of people can be, for example, any number that allows the user to easily understand the disease risk, such as 10 people or 100 people, but there are no limitations on the representation. X is, for example, an arbitrary numerical value corresponding to the representation format of the disease risk. According to this disclosure, for example, the disease risk is converted into a representation format that is familiar to the user, so the user can easily understand the disease risk.

[0036] The categorized risk information is, for example, information that categorizes the risk of onset into multiple categories. The prediction unit 11 may generate the categorized risk information based, for example, on categorization conversion criterion information. The categorization conversion criterion information is, for example, information regarding the correspondence between the risk of onset and the categories. The categorized risk information may also be, for example, information that categorizes the risk of onset into categories such as Low risk, Medium Low risk, Medium risk, Medium High risk, and High risk. The categorized risk information may also be, for example, information that categorizes the risk of onset into categories such as A rank, B rank, and C rank. According to this disclosure, for example, by categorizing the risk of onset, users can easily understand the risk of onset.

[0037] The generation unit 12 generates explanatory information for the target disease based on the combination of disease risk information (S2, generation procedure). The generation unit 12 can also be called, for example, the explanatory information generation unit. In this case, the generation procedure can also be called, for example, the explanatory information generation procedure.

[0038] The explanatory information is information relating to an explanation of the target disease. The explanatory information may, for example, be information relating to an explanation of the state of the target disease. The explanatory information may, for example, be information relating to an explanation of the current state and future state of the target disease. As will be described later, the explanation of the target disease may include, for example, an explanation of the risk of developing the target disease and an explanation of the prevention of the target disease.

[0039] The aforementioned target disease may be, for example, a disease included in the combination of disease risk information, or a disease other than those included in the combination of disease risk information. Specifically, if the diseases included in the combination of disease risk information are disease A and disease B, the aforementioned target disease may be at least one of disease A and disease B, or disease C other than disease A and disease B. Disease A, disease B, and disease C represent, for example, any different diseases from the aforementioned diseases. For example, the description of the aforementioned diseases can be used to describe the aforementioned target disease.

[0040] The explanatory information may include, for example, other explanatory information. The other explanatory information may include, for example, information relating to the explanation of the disease included in the combination of the disease risk information. The other explanatory information may include, for example, an explanation relating to the disease risk included in the combination of disease risk information.

[0041] As mentioned above, the explanatory information may include, for example, an explanation of the risk of developing the target disease. The explanation of the risk of developing the target disease may be, for example, an explanation of the risk of developing the target disease when the combination of the risk information is a predetermined combination. The predetermined combination is not particularly limited and can be appropriately selected depending on the type of target disease and the type of explanation.

[0042] The explanation regarding the risk of developing the aforementioned target disease may include, for example, an explanation regarding the possibility that the risk of developing the aforementioned target disease may increase when the combination of the risk information is a predetermined combination. The risk of developing the aforementioned target disease may increase, for example, in response to the combination of the risk information. Specifically, it is known that the risk of developing cerebrovascular disease, which is an example of the aforementioned target disease, may increase, for example, in combinations where the risk of developing cerebrovascular disease and dementia are each above a predetermined risk. Also, it is known that the risk of developing cerebrovascular disease, which is an example of the aforementioned target disease, may increase, for example, in combinations where the risk of developing cerebrovascular disease and chronic renal failure are each above a predetermined risk. Therefore, according to this disclosure, for example, the risk of developing the aforementioned target disease corresponding to the combination of the risk information can be made to recognize. It should be noted that the explanation regarding the risk of developing the aforementioned target disease is not limited in any way and can be appropriately selected depending on the combination of the risk information and the aforementioned target disease.

[0043] The explanation regarding the risk of developing the aforementioned target disease may include, for example, an explanation of diseases that may increase the risk of developing the aforementioned target disease when the combination of risk information is a predetermined combination. The risk of developing the aforementioned target disease may increase, for example, when certain diseases coexist in the combination of risk information. Specifically, it is known that the risk of developing cerebrovascular disease, which is an example of the aforementioned target disease, may increase when hypertension or dyslipidemia coexists in a combination where the risk of developing cerebrovascular disease and dementia are each above a predetermined risk level. Therefore, according to this disclosure, for example, it is possible to identify diseases that may increase the risk of developing the aforementioned target disease when they coexist with the aforementioned combination of risk information. The explanation regarding the risk of developing the aforementioned target disease is not limited in any way and can be appropriately selected depending on the combination of risk information and the aforementioned target disease.

[0044] The generation unit 12 may, for example, generate the explanatory information from the combination of disease risk information based on the correspondence information. The correspondence information is, for example, information that links the explanatory information to the combination of disease risk information. The correspondence information may be, for example, information that links the explanatory information to the combination of disease risk information in a tree format such as a hierarchical database, or in a tabular format such as a relational database. Note that, for example, if the correspondence information is information that links the explanatory information to the combination of disease risk information, it can also be said to be correspondence information that targets the combination of disease risk information.

[0045] In the following, an example of generating the explanatory information based on the combination of disease risk information in this disclosure will be explained using Figure 4, but the generation of the explanatory information based on the combination of disease risk information is not limited in any way to the above description. Figure 4 shows an example of the corresponding information when the combination of disease risk information is the disease risk information for disease A, the disease risk information for disease B, and the disease risk information for disease C. Figure 4 describes the case when the disease risk information for disease A is Low risk, Medium risk, or High risk, the disease risk information for disease B is Low risk or High risk, and the disease risk information for disease C is Low risk or High risk.

[0046] First, Figure 4 illustrates an example of generating explanatory information based on the combination of disease risk information in this disclosure, where the disease risk information for disease A is Low risk and the disease risk information for disease B is High risk. In this case, the generation unit 12 identifies the explanatory information linked to the combination of disease risk information based on the corresponding information shown in Figure 4. In Figure 4, the explanatory information linked to the combination of disease risk information includes, for example, the statement, "The risk of disease A is low, but the risk of disease B is high, so please pay attention to X1." Therefore, the generation unit 12 generates this statement as the explanatory information. The "X1" included in the explanatory information refers to, for example, a cautionary note when the disease risk information for disease A is Low risk and the disease risk information for disease B is High risk. The cautionary note can be set appropriately according to the combination of disease risk information. The cautionary note may include, for example, a cautionary note regarding the disease, a cautionary note regarding lifestyle habits, etc., but there are no limitations on the content. The precautions regarding the aforementioned diseases may, for example, be appropriately set out of various diseases that require particular attention, according to the combination of the aforementioned risk information. The precautions regarding lifestyle habits may, for example, be appropriately set out of various lifestyle habits that require particular attention for improvement, according to the combination of the aforementioned risk information. Specifically, the precautions regarding lifestyle habits may, for example, be appropriately set out of dietary habits and exercise habits that require particular attention for improvement, according to the combination of the aforementioned risk information, but there are no limitations on the description.

[0047] Furthermore, Figure 4 illustrates an example of generating the explanatory information based on the combination of disease risk information in this disclosure, where the disease risk information for disease A is Low risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is High risk. In this case, the generation unit 12 identifies the explanatory information linked to the combination of disease risk information based on the corresponding information shown in Figure 4. In Figure 4, the explanatory information linked to the combination of disease risk information includes, for example, the statement, "The risk for disease A is low, but the risk for disease C is high, so please pay attention to 'X2'." Therefore, the generation unit 12 generates this statement as the explanatory information. The "X2" included in the explanatory information indicates, for example, a cautionary note in the case where the disease risk information for disease A is Low risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is High risk. The cautionary note can, for example, be based on the explanation described above.

[0048] Furthermore, Figure 4 illustrates an example of generating the explanatory information based on the combination of disease risk information in this disclosure, where the disease risk information for disease A is Low risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is Low risk. In this case, the generation unit 12 identifies the explanatory information linked to the combination of disease risk information based on the corresponding information shown in Figure 4. In Figure 4, the explanatory information linked to the combination of disease risk information includes, for example, the statement, "The risk of disease A is low. Please strive to maintain the current condition." Therefore, the generation unit 12 generates this statement as the explanatory information.

[0049] Next, Figure 4 illustrates an example of generating the explanatory information based on the combination of disease risk information in this disclosure, where the disease risk information for disease A is Medium risk and the disease risk information for disease B is High risk. In this case, the generation unit 12 identifies the explanatory information linked to the combination of disease risk information based on the corresponding information shown in Figure 4. In Figure 4, the explanatory information linked to the combination of disease risk information includes, for example, the statement, "The risk for disease A is moderate, but the risk for disease B is high, so please pay attention to 'X3'." Therefore, the generation unit 12 generates this statement as the explanatory information. The "X3" included in the explanatory information indicates, for example, a cautionary note when the disease risk information for disease A is Medium risk and the disease risk information for disease B is High risk. The cautionary note can, for example, be based on the explanation described above.

[0050] Furthermore, Figure 4 illustrates an example of generating the explanatory information based on the combination of disease risk information in this disclosure, where the disease risk information for disease A is Medium risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is High risk. In this case, the generation unit 12 identifies the explanatory information linked to the combination of disease risk information based on the corresponding information shown in Figure 4. In Figure 4, the explanatory information linked to the combination of disease risk information includes, for example, the statement, "The risk for disease A is moderate, but the risk for disease C is high, so please pay attention to 'X4'." Therefore, the generation unit 12 generates this statement as the explanatory information. The "X4" included in the explanatory information indicates, for example, a cautionary note in the case where the disease risk information for disease A is Medium risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is High risk. The cautionary note can, for example, be based on the explanation described above.

[0051] Furthermore, Figure 4 illustrates an example of generating the explanatory information based on the combination of disease risk information in this disclosure, where the disease risk information for disease A is Medium risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is Low risk. In this case, the generation unit 12 identifies the explanatory information linked to the combination of disease risk information based on the corresponding information shown in Figure 4. In Figure 4, the explanatory information linked to the combination of disease risk information includes, for example, the statement, "The risk for disease A is moderate. Please pay attention to 'X5'." Therefore, the generation unit 12 generates this statement as the explanatory information. The "X5" included in the explanatory information refers to, for example, a cautionary note in the case where the disease risk information for disease A is Medium risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is Low risk. The cautionary note can, for example, be based on the explanation described above.

[0052] Next, Figure 4 illustrates an example of generating the explanatory information based on the combination of disease risk information in this disclosure, where the disease risk information for disease A is High risk and the disease risk information for disease B is High risk. In this case, the generation unit 12 identifies the explanatory information linked to the combination of disease risk information based on the corresponding information shown in Figure 4. In Figure 4, the explanatory information linked to the combination of disease risk information includes, for example, the statement, "Because not only is the risk for disease A high, but the risk for disease B is also high, please pay attention to "X6"." Therefore, the generation unit 12 generates this statement as the explanatory information. The "X6" included in the explanatory information indicates, for example, a cautionary note when the disease risk information for disease A is High risk and the disease risk information for disease B is High risk. The cautionary note can, for example, be based on the explanation described above.

[0053] Furthermore, Figure 4 illustrates an example of generating the explanatory information based on the combination of disease risk information in this disclosure, where the disease risk information for disease A is High risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is High risk. In this case, the generation unit 12 identifies the explanatory information linked to the combination of disease risk information based on the corresponding information shown in Figure 4. In Figure 4, the explanatory information linked to the combination of disease risk information includes, for example, the statement, "Because not only is the risk for disease A high, but the risk for disease C is also high, please pay attention to "X7"." Therefore, the generation unit 12 generates this statement as the explanatory information. The "X7" included in the explanatory information indicates, for example, a cautionary note in the case where the disease risk information for disease A is High risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is High risk. The cautionary note can, for example, be based on the explanation described above.

[0054] Furthermore, Figure 4 illustrates an example of generating the explanatory information based on the combination of disease risk information in this disclosure, where the disease risk information for disease A is High risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is Low risk. In this case, the generation unit 12 identifies the explanatory information linked to the combination of disease risk information based on the corresponding information shown in Figure 4. In Figure 4, the explanatory information linked to the combination of disease risk information includes, for example, the statement, "The risk of disease A is high. Please pay attention to "X8". Therefore, the generation unit 12 generates this statement as the explanatory information. The "X8" included in the explanatory information indicates, for example, a cautionary note when the disease risk information for disease A is High risk, the disease risk information for disease B is Low risk, and the disease risk information for disease C is Low risk. The cautionary note can, for example, be based on the explanation described above.

[0055] Furthermore, Table 1 illustrates another example of generating the explanatory information based on the combination of disease risk information in this disclosure, but the generation of the explanatory information based on the combination of disease risk information is not limited to this description. Table 1 shows another example of the corresponding information when the combination of disease risk information is a combination of the disease risk information for cerebrovascular disease and the disease risk information for dementia. Table 1 shows the case when the disease risk information for dementia is Low risk or High risk, and the disease risk information for cerebrovascular disease is Low risk, Medium Low risk, Medium High risk, or High risk.

[0056] First, Table 1 describes another example of generating the explanatory information based on the combination of onset risk information in this disclosure, where the onset risk information for cerebrovascular disease is Medium Low risk and the onset risk information for dementia is High risk. In this case, the generation unit 12 identifies the field corresponding to the combination of onset risk information based on the corresponding information described in Table 1. In Table 1, the field contains, for example, the explanatory information, "If you have been diagnosed with hypertension or dyslipidemia, your risk of developing cerebrovascular disease in the future may increase" (the part of the field in Table 1 with a gray background). Therefore, the generation unit 12 generates, for example, the content of the field as the explanatory information.

[0057] Next, Table 1 illustrates an example of generating the explanatory information based on the combination of disease risk information described herein, where the risk information for cerebrovascular disease is Medium High risk or higher, and the risk information for dementia is High risk. In this case, the generation unit 12 identifies a field corresponding to the combination of disease risk information based on the corresponding information described in Table 1. In Table 1, the field contains, for example, the explanatory information, "Cerebrovascular disease and dementia may mutually increase the risk of developing each other" (the part of the field in Table 1 with a gray background). Therefore, the generation unit 12 generates the content of the field as the explanatory information.

[0058] [Table 1]

[0059] The generation unit 12 may generate the explanatory information using, for example, an explanatory information generation model. The explanatory information generation model is, for example, a model that generates the explanatory information by inputting a combination of the disease risk information. The explanatory information generation model is, for example, a trained model generated by machine learning using the combination of disease risk information as training data, so that it generates the explanatory information when a combination of disease risk information is input.

[0060] The explanatory information generation model may be stored, for example, in the memory 102 or storage device 104 of the device of this disclosure, or in the memory or storage device of a device other than the device of this disclosure.

[0061] As mentioned above, the explanatory information may include, for example, an explanation regarding the prevention of the target disease. The prevention of the target disease may include, for example, actions that can prevent the onset of the target disease. Examples of such actions include diet, exercise, sleep, smoking cessation, alcohol restriction, brain training, and oral care. These actions can be appropriately selected, for example, in response to the target disease. The explanation regarding the prevention of the target disease may be generated, for example, based on correspondence information linking the combination of onset risk information with the actions. Alternatively, the explanation regarding the prevention of the target disease may be generated, for example, based on correspondence information linking the corresponding disease with the actions.

[0062] The prediction unit 11 may, for example, generate biological state information relating to the predicted biological state based on the biological information (S1A, prediction procedure). The prediction unit 11 may also include, for example, a biological state prediction unit that predicts the biological state and a biological state information generation unit that generates the biological state information. In this case, the prediction procedure may include, for example, a biological state prediction procedure and a biological state information generation procedure.

[0063] The biological state information is, for example, information about the biological state predicted based on the biological information. Examples of the biological state include glucose tolerance, liver fat, effects of alcohol consumption, effects of smoking, maximum oxygen uptake, visceral fat, and resting metabolic rate. The biological state may be predicted by, for example, a known method. Examples of the known method include a regression equation relating to the biological state and a biological state prediction model relating to the biological state. The regression equation may be, for example, a simple regression equation or a multiple regression equation. Examples of the regression equation may be generated by multivariate regression analysis such as logistic regression analysis. Examples of the biological state prediction model may be generated by machine learning.

[0064] The prediction unit 11 may predict the biological state using, for example, a known regression equation. The regression equation is, for example, an equation that takes the biological information as an explanatory variable and outputs the biological state as the dependent variable.

[0065] The prediction unit 11 may predict the biological state using, for example, a known biological state prediction model. The biological state prediction model is, for example, a model that predicts the biological state by inputting the biological information. The biological state prediction model is, for example, a trained model generated by machine learning using the biological information as training data, which predicts the biological state when the biological information is input.

[0066] The regression equation may be stored, for example, in the memory 102 or storage device 104 of the device of this disclosure, or in the memory or storage device of a device other than the device of this disclosure. The biological state prediction model may also be stored, for example, in the memory 102 or storage device 104 of the device of this disclosure, or in the memory or storage device of a device other than the device of this disclosure.

[0067] The generation unit 12 may, for example, generate the explanatory information based on the combination of disease risk information and the biological state information (S2A, generation procedure). The generation unit 12 can also be called, for example, the explanatory information generation unit. In this case, the generation procedure can also be called, for example, the explanatory information generation procedure.

[0068] The generation unit 12 may, for example, generate the explanatory information from the combination of disease risk information and the biological state information based on the correspondence information. The correspondence information is, for example, information that links the explanatory information to the combination of disease risk information and the biological state information. The correspondence information may be, for example, information that links the explanatory information to the combination of disease risk information and the biological state information in a tree format such as a hierarchical database, or in a tabular format such as a relational database. Note that, for example, if the correspondence information is information that links the explanatory information to the combination of disease risk information and the biological state information, it can also be said to be correspondence information that targets the combination of disease risk information and the biological state information.

[0069] The generation unit 12 may generate the explanatory information using, for example, an explanatory information generation model. The explanatory information generation model is, for example, a model that generates the explanatory information by inputting a combination of disease risk information and the biological state information. The explanatory information generation model is, for example, a trained model generated by machine learning using the combination of disease risk information and the biological state information as training data, so that it generates the explanatory information when the combination of disease risk information and the biological state information are input.

[0070] The explanatory information generation model may be stored, for example, in the memory 102 or storage device 104 of the device of this disclosure, or in the memory or storage device of a device other than the device of this disclosure.

[0071] The output unit 13 outputs the explanatory information (S3, output procedure). The output unit 13 can also be called, for example, the explanatory information output unit. In this case, the output procedure can also be called, for example, the explanatory information output procedure.

[0072] The output unit 13 may output the explanatory information to a template, such as a report. The template may output only the explanatory information, or it may output an explanation based on information other than the explanatory information in addition to the explanatory information. Examples of information other than the explanatory information include the biological information, the risk of developing the disease, and the risk of developing the disease.

[0073] The output unit 13 may output items related to the explanatory information in a highlighted manner. Examples of such items include the name of the disease, the name of the biological information, and the name of the biological state information. The output unit 13 may also output parts of the biological body related to the explanatory information in a highlighted manner in the model diagram of the biological body. Examples of such model diagrams include those that allow the arrangement of the internal organs of the biological body to be understood. Examples of such model diagrams include those that show the biological body viewed from the front.

[0074] The output unit 13 may output, for example, the disease risk information in addition to the explanatory information. The output unit 13 may output the disease risk information as text data or as image data. In the latter case, the output unit 13 may output the disease risk information in the form of, for example, a one-dimensional coordinate axis, a bar graph, a pie chart, or a band graph.

[0075] The output unit 13 may, for example, output the explanatory information to the memory 102 or storage device 104 of the device of the disclosure, or to the memory or storage device of a device other than the device of the disclosure. In this case, the explanatory information may, for example, be stored in the memory 102 or storage device 104 of the device of the disclosure, or to the memory or storage device of a device other than the device of the disclosure. The output unit 13 may, for example, output the explanatory information to the output device 106 of the device of the disclosure. The output unit 13 may, for example, output the explanatory information to the external device.

[0076] The method for providing explanatory information in this disclosure (hereinafter also referred to as the "method of this disclosure") is a method that is implemented by, for example, replacing each "procedure" in the program of this disclosure with a "process". Specifically, the method of this disclosure includes a prediction process, a generation process, and an output process. The prediction process generates disease risk information for multiple diseases based on biological information, relating to the predicted disease risk for each disease. The generation process generates explanatory information for the target disease based on a combination of the disease risk information. The output process outputs the explanatory information. The method of this disclosure can be implemented, for example, using the apparatus 10 of this disclosure shown in Figure 1 or Figure 2. However, the method of this disclosure is not limited to, for example, a method using the apparatus 10 of this disclosure. The method of this disclosure can be implemented by referring to, for example, the descriptions in the program and apparatus of this disclosure.

[0077] As described above, according to the explanatory information provision program of this disclosure, the prediction procedure generates disease risk information for multiple diseases based on biological information, the generation procedure generates explanatory information for the target disease based on combinations of the disease risk information, and the output procedure outputs the explanatory information.Therefore, according to this disclosure, it is possible to provide explanatory information that takes into account the relationships between diseases.In addition, according to this disclosure, for example, explanatory information can be classified according to combinations of disease risk information for each of multiple diseases.In addition, according to this disclosure, for example, by combining disease risk information for each of multiple diseases, it is possible to provide insights that cannot be obtained from disease risk information for a single disease.In addition, according to this disclosure, for example, it is possible to provide information on the overall health status of the organism based on disease risk information for each of multiple diseases.

[0078] [Embodiment 3] An example of how the device described herein can be used is explained below. In the following explanation, the example given is when the combination of disease risk information is a combination of disease risk information for cerebrovascular disease and disease risk information for dementia, but this disclosure is not limited to the following explanation.

[0079] The device of this disclosure, for example, in healthcare services, generates reports that include explanations considering the relationships between diseases. Specifically, the service provider, for example, collects venous blood from a patient at a medical institution. The service provider, for example, measures the concentration of protein in plasma obtained by centrifugation from the venous blood using an analyzer. The device of this disclosure, for example, obtains the measurement result of the protein concentration in the plasma from the analyzer as biometric information via a communication network. The device of this disclosure, for example, inputs the biometric information into a cerebrovascular disease risk prediction model and predicts that the patient's risk of developing cerebrovascular disease within four years is 0.3. The device of this disclosure, for example, inputs the biometric information into a dementia risk prediction model and predicts that the patient's risk of developing dementia within five years is 0.9. Based on the prediction result that the risk of developing cerebrovascular disease is 0.3, the device of this disclosure generates disease risk information indicating that the patient's risk of developing cerebrovascular disease is a Medium Low risk. The device of this disclosure generates disease risk information indicating that the patient's cognitive function is at high risk, based on a predicted result that the risk of developing dementia is 0.9. The device of this disclosure identifies a field in the corresponding information shown in Table 1 that corresponds to the combination of disease risk information (i.e., a combination of Medium Low risk, which is the risk of developing cerebrovascular disease, and High risk, which is the risk of developing dementia). The device of this disclosure generates explanatory information for the target disease, cerebrovascular disease, with the statement "If you have been diagnosed with hypertension or dyslipidemia, your future risk of developing cerebrovascular disease may increase" in the identified field. The device of this disclosure outputs the explanatory information in the explanatory section of a healthcare service report template, for example, to create a report for the patient. The patient, for example, after receiving the report from the service provider, can read through the explanatory section and recognize that in order to reduce the risk of developing cerebrovascular disease, it is necessary to take measures to prevent hypertension and dyslipidemia.Thus, according to the device of this disclosure, for example, by combining risk information for the onset of multiple diseases, it is possible to provide patients with an understanding that cannot be obtained from risk information for the onset of a single disease.

[0080] This disclosure allows for the provision of explanations that take into account the relationships between diseases. Furthermore, this disclosure allows for the classification of explanations based on combinations of disease-specific risk information for multiple diseases. Moreover, this disclosure allows for the provision of insights that cannot be obtained from disease-specific risk information for a single disease by combining disease-specific risk information for multiple diseases. Furthermore, this disclosure allows for the provision of information on the overall health status of a living organism based on disease-specific risk information for multiple diseases.

[0081] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. Various modifications to the structure and details of the present disclosure can be made as can be understood by those skilled in the art within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0082] <Note> Some or all of the above embodiments may be described as follows, but are not limited to the following: (Note 1) Includes prediction procedure, generation procedure, and output procedure, The aforementioned prediction procedure generates disease risk information for multiple diseases based on biological information, with a predicted risk of onset for each disease. The above generation procedure generates explanatory information for the target disease based on the combination of the disease risk information, The output procedure outputs the explanatory information. A program that provides explanatory information to cause a computer to perform each of the above procedures. (Note 2) The aforementioned explanatory information includes an explanation regarding the risk of developing the aforementioned target disease. The explanatory information provision program described in Appendix 1. (Note 3) The explanation regarding the risk of developing the aforementioned target disease includes an explanation regarding the possibility that the risk of developing the aforementioned target disease may increase when the combination of the risk information is a predetermined combination. The explanatory information provision program described in Appendix 2. (Note 4) The explanation regarding the risk of developing the aforementioned target disease includes an explanation regarding diseases that may increase the risk of developing the aforementioned target disease when the combination of risk information is a predetermined combination. The explanatory information provision program described in Appendix 2 or 3. (Note 5) The aforementioned explanatory information includes an explanation regarding the prevention of the aforementioned target disease. A program providing explanatory information as described in any of the appendices 1 to 4. (Note 6) The prediction procedure generates biological state information relating to the predicted biological state based on the biological information, The generation procedure generates the explanatory information based on the combination of the disease risk information and the biological state information. A program providing explanatory information as described in any of the appendices 1 through 5. (Note 7) The aforementioned risk information includes categorized risk information, which divides the risk of onset into multiple categories. A program providing explanatory information as described in any of the appendices 1 through 6. (Note 8) Including a prediction unit, a generation unit, and an output unit, The prediction unit generates disease risk information for multiple diseases based on biological information, relating to the predicted risk of onset for each disease. The generation unit generates explanatory information about the target disease based on the combination of the disease risk information. The output unit outputs the explanatory information. A device that provides explanatory information. (Note 9) The aforementioned explanatory information includes an explanation regarding the risk of developing the aforementioned target disease. The explanatory information provision device described in Appendix 8. (Note 10) The explanation regarding the risk of developing the aforementioned target disease includes an explanation regarding the possibility that the risk of developing the aforementioned target disease may increase when the combination of the risk information is a predetermined combination. The explanatory information provision device described in Appendix 9. (Note 11) The explanation regarding the risk of developing the aforementioned target disease includes an explanation regarding diseases that may increase the risk of developing the aforementioned target disease when the combination of risk information is a predetermined combination. An explanatory information providing device as described in Appendix 9 or 10. (Note 12) The aforementioned explanatory information includes an explanation regarding the prevention of the aforementioned target disease. An explanatory information providing device as described in any of the appendices 8 to 11. (Note 13) The prediction unit generates biological state information relating to the predicted biological state based on the biological information, The generation unit generates the explanatory information based on the combination of the disease risk information and the biological state information. An explanatory information providing device as described in any of the appendices 8 to 12. (Note 14) The aforementioned risk information includes categorized risk information, which divides the risk of onset into multiple categories. An explanatory information providing device as described in any of the appendices 8 to 13. (Note 15) Including a prediction process, a generation process, and an output process, The aforementioned prediction step generates disease risk information for multiple diseases based on biological information, relating to the predicted risk of onset for each disease. The generation process generates explanatory information about the target disease based on the combination of disease risk information. The output step outputs the explanatory information. A method for providing explanatory information, in which each of the aforementioned steps is performed by a computer. (Note 16) The aforementioned explanatory information includes an explanation regarding the risk of developing the aforementioned target disease. The method of providing explanatory information as described in Appendix 15. (Note 17) The explanation regarding the risk of developing the aforementioned target disease includes an explanation regarding the possibility that the risk of developing the aforementioned target disease may increase when the combination of the risk information is a predetermined combination. The method of providing explanatory information as described in Appendix 16. (Note 18) The explanation regarding the risk of developing the aforementioned target disease includes an explanation regarding diseases that may increase the risk of developing the aforementioned target disease when the combination of risk information is a predetermined combination. The method of providing explanatory information as described in Appendix 16 or 17. (Note 19) The aforementioned explanatory information includes an explanation regarding the prevention of the aforementioned target disease. The method of providing explanatory information as described in any of the appendices 15 to 18. (Note 20) The prediction step generates biological state information relating to the predicted biological state based on the biological information, The generation step generates the explanatory information based on the combination of the disease risk information and the biological state information. The method of providing explanatory information as described in any of the appendices 15 to 19. (Note 21) The aforementioned risk information includes categorized risk information, which divides the risk of onset into multiple categories. The method of providing explanatory information as described in any of the appendices 15 to 20. (Note 22) Includes prediction procedure, generation procedure, and output procedure, The aforementioned prediction procedure generates disease risk information for multiple diseases based on biological information, with a predicted risk of onset for each disease. The above generation procedure generates explanatory information for the target disease based on the combination of the disease risk information, The output procedure outputs the explanatory information. A computer-readable recording medium containing a program that provides explanatory information for causing a computer to perform each of the aforementioned procedures. (Note 23) The aforementioned explanatory information includes an explanation regarding the risk of developing the aforementioned target disease. Recording medium as described in Appendix 22. (Note 24) The explanation regarding the risk of developing the aforementioned target disease includes an explanation regarding the possibility that the risk of developing the aforementioned target disease may increase when the combination of the risk information is a predetermined combination. Recording medium as described in Appendix 23. (Note 25) The explanation regarding the risk of developing the aforementioned target disease includes an explanation regarding diseases that may increase the risk of developing the aforementioned target disease when the combination of risk information is a predetermined combination. Recording medium as described in Appendix 23 or 24. (Note 26) The aforementioned explanatory information includes an explanation regarding the prevention of the aforementioned target disease. A recording medium as described in any of the appendices 22 to 25. (Note 27) The prediction procedure generates biological state information relating to the predicted biological state based on the biological information, The generation procedure generates the explanatory information based on the combination of the disease risk information and the biological state information. A recording medium as described in any of the appendices 22 to 26. (Note 28) The aforementioned risk information includes categorized risk information, which divides the risk of onset into multiple categories. A recording medium as described in any of appendices 22 to 27. [Industrial applicability]

[0083] This disclosure allows for explanations that take into account the relationships between diseases. Therefore, this disclosure can be widely and usefully used in the medical and healthcare fields, among others. [Explanation of symbols]

[0084] 10. Explanatory Information Provision Device 11 Prediction Section 12 Generation part 13 Output section 101 Central Processing Unit 102 memory 103 Bus 104 Storage device 105 Input device 106 Output device 107 Communication devices

Claims

1. Includes prediction procedure, generation procedure, and output procedure, The aforementioned prediction procedure generates disease risk information for multiple diseases based on biological information, with a predicted risk of onset for each disease. The above generation procedure generates explanatory information for the target disease based on the combination of the disease risk information, The output procedure outputs the explanatory information. A program that provides explanatory information to cause a computer to perform each of the above procedures.

2. The aforementioned explanatory information includes an explanation regarding the risk of developing the aforementioned target disease. The explanatory information provision program according to claim 1.

3. The explanation regarding the risk of developing the aforementioned target disease includes an explanation regarding the possibility that the risk of developing the aforementioned target disease may increase when the combination of the risk information is a predetermined combination. The explanatory information provision program according to claim 2.

4. The explanation regarding the risk of developing the aforementioned target disease includes an explanation regarding diseases that may increase the risk of developing the aforementioned target disease when the combination of risk information is a predetermined combination. The explanatory information provision program according to claim 2.

5. The aforementioned explanatory information includes an explanation regarding the prevention of the aforementioned target disease. The explanatory information provision program according to claim 1.

6. The prediction procedure generates biological state information relating to the predicted biological state based on the biological information, The generation procedure generates the explanatory information based on the combination of the disease risk information and the biological state information. The explanatory information provision program according to claim 1.

7. The aforementioned risk information includes categorized risk information, which divides the risk of onset into multiple categories. An explanatory information provision program according to any one of claims 1 to 6.

8. Including a prediction unit, a generation unit, and an output unit, The prediction unit generates disease risk information for multiple diseases based on biological information, relating to the predicted risk of onset for each disease. The generation unit generates explanatory information about the target disease based on the combination of the disease risk information. The output unit outputs the explanatory information. A device that provides explanatory information.

9. Including a prediction process, a generation process, and an output process, The aforementioned prediction step generates disease risk information for multiple diseases based on biological information, relating to the predicted risk of onset for each disease. The generation process generates explanatory information about the target disease based on the combination of disease risk information. The output step outputs the explanatory information. A method for providing explanatory information, wherein each of the aforementioned steps is performed by a computer.

10. Includes prediction procedure, generation procedure, and output procedure, The aforementioned prediction procedure generates disease risk information for multiple diseases based on biological information, with a predicted risk of onset for each disease. The above generation procedure generates explanatory information for the target disease based on the combination of the disease risk information, The output procedure outputs the explanatory information. A computer-readable recording medium containing a program that provides explanatory information for causing a computer to perform each of the aforementioned procedures.

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