Information processing device, information processing method, and program

Abstract

The objective is to provide an information processing device, an information processing method, and a program that can dynamically predict the risk of diseases, etc., based on genetic information and lifestyle information. [Solution] An information processing device comprising: a receiving unit that receives input or changes to lifestyle variables; and a prediction unit that predicts the user's risk of disease based on genetic information and the lifestyle variables, wherein the prediction unit updates the risk based on the changes to the lifestyle variables received by the receiving unit.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and a program.

Background Art

[0002] In recent years, technologies for predicting an individual's health risk by combining genetic information and lifestyle data have attracted attention. However, conventional health risk prediction models often use static data and have a problem that it is difficult to dynamically reflect changes in an individual's lifestyle (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Although existing prediction models are based on genetic data and some lifestyle factors, the function of immediately evaluating how the risk changes when a user changes their lifestyle is limited. For example, when individually simulating the effects of weight gain or loss and drinking / smoking habits on health risk, it is generally necessary to manually analyze the data, which is troublesome for users.

[0005] The present invention has been made in view of the above problems, and an object thereof is to provide an information processing apparatus, an information processing method, and a program capable of dynamically predicting risks related to diseases and the like based on genetic information and lifestyle information.

Means for Solving the Problems

[0006] That is, the present invention is as follows. 〔1〕 A receiving unit that accepts input or changes to lifestyle variables, It includes a prediction unit that predicts the user's risk of disease based on genetic information and the lifestyle variables, The prediction unit updates the risk based on the changes in the lifestyle variables received by the receiving unit. Information processing device. [2] The lifestyle variables include the user's physiological information, the user's lifestyle information, and information regarding the user's and their close relatives' medical history. The information processing device described in [1]. [3] The aforementioned physiological information includes age, sex, body size, vital signs, or information from blood tests. The information processing device described in [1] or [2]. [4] The aforementioned lifestyle information includes information on eating habits, exercise habits, sleep habits, smoking or passive smoking, and information on alcohol consumption. An information processing device as described in any one of items [1] to [3]. [5] The aforementioned diseases include myocardial infarction, stroke, type 2 diabetes, hypertension, cirrhosis due to hepatitis C, chronic kidney disease, colorectal cancer, lung cancer, and stomach cancer. An information processing device as described in any one of items [1] to [4]. [6] The receiving unit has a display instruction unit that, when it receives a change in the lifestyle variable, instructs the receiving unit to display the risk before the update and the risk after the update in a way that allows for comparison. An information processing device as described in any one of items [1] to [5]. [7] The prediction unit includes a display instruction unit that instructs the unit to display the risk associated with the user's age, as well as the future risk as time progresses. An information processing device as described in any one of items [1] to [6]. [8] In an information processing device, A step to accept input or changes to lifestyle variables, Predicting the risk of a disease for a user based on genetic information and the lifestyle variables; updating the risk based on the changes in the lifestyle variables received; and causing the execution of the steps. Program. 〔9〕 An information processing apparatus receiving an input or change of lifestyle variables; predicting the risk of a disease for a user based on genetic information and the lifestyle variables; updating the risk based on the changes in the lifestyle variables received; and executing the steps. Information processing method.

Effect of the Invention

[0007] According to the present invention, it is possible to provide an information processing apparatus, an information processing method, and a program that can dynamically predict risks related to diseases and the like based on genetic information and lifestyle information.

Brief Description of the Drawings

[0008] [Figure 1] It is a schematic diagram showing the system of this embodiment. [Figure 2] It is a diagram showing the configuration of the server in this embodiment. [Figure 3] It is a diagram showing the configuration of the user terminal in this embodiment. [Figure 4A] It is a diagram showing an example of the screen D1 of the user terminal in this embodiment. [Figure 4B] It is a diagram showing an example of the screen D2 of the user terminal in this embodiment. [Figure 4C] It is a diagram showing an example of the screen D3 of the user terminal in this embodiment. [Figure 4D] It is a diagram showing an example of the screen D4 of the user terminal in this embodiment. [Figure 4E] It is a diagram showing an example of the screen D5 of the user terminal in this embodiment. [Figure 5]This is an example of a sequence diagram showing the processing performed by the information processing apparatus in the present embodiment. [Figure 6] This is a diagram showing a comparison example of risk curves obtained by the method shown in the modified example.

Mode for Carrying Out the Invention

[0009] Hereinafter, embodiments of the present invention (hereinafter referred to as "the present embodiment") will be described in detail. However, the present invention is not limited thereto, and various modifications are possible without departing from the gist thereof.

[0010] 1. System FIG. 1 is a schematic diagram showing the system of the present embodiment. According to the system of the present embodiment, it is possible to realize dynamic prediction models of various diseases and symptoms based on genetic information and lifestyle habits, and user-oriented services based on them. As shown in FIG. 1, the system 1 of the present embodiment may include a server 100 and a user terminal 200, and may be configured to be communicable with an external server 300. The server 100, the user terminal 200, and the external server 300 may be connected via a network N.

[0011] The server 100 receives an input of lifestyle variables from the user terminal 200, predicts the risk of the user's disease based on the genetic information and the lifestyle variables, and instructs the display device of the user terminal 200 to display information regarding the risk. Further, the server 100 may receive a change in the lifestyle variables from the user terminal 200, update the risk based on the genetic information and the changed lifestyle variables, and instruct the display device of the user terminal 200 to display the risks before and after the update in a comparable manner.

[0012] The user terminal 200 is a terminal used by the user, and may input lifestyle variables to the server 100 or change the input lifestyle variables. The display device of the user terminal 200 may also display and control the user's risk of disease predicted by the server 100 based on genetic information and lifestyle variables, in accordance with the display instructions of the server 100. Furthermore, when an input lifestyle variable is changed, the display device of the user terminal 200 may also display the risk of disease before and after the change in lifestyle variables in a comparative manner, in accordance with the display instructions of the server 100.

[0013] The external server 300 may be a publicly available database that records genes, variants, proteins, compounds, and medical publications.

[0014] Network N is not particularly limited, but may include ad-hoc networks, intranets, extranets, virtual private networks (VPNs), local area networks (LANs), wireless LANs (WLANs), wide area networks (WANs), wireless WANs (WWANs), metropolitan area networks (MANs), parts of the internet, parts of public switched telephone networks (PSTNs), mobile phone networks, ISDNs, wireless LANs, LTE, CDMA, Bluetooth®, satellite communications, etc., and these may be combined. Network N may include one or more networks.

[0015] This system allows users to easily assess how their risk of major lifestyle-related diseases such as myocardial infarction, stroke, type 2 diabetes, hypertension, cirrhosis due to hepatitis C, chronic kidney disease, colorectal cancer, lung cancer, and stomach cancer changes when they alter lifestyle variables such as weight gain or loss, and whether or not they drink alcohol or smoke. Therefore, users can learn about their disease risk in relation to their lifestyle differences, gain an opportunity to consider their health management more seriously, and take more effective preventive measures.

[0016] The following description will use the case where the information processing device of this embodiment is a server as an example, but this embodiment is not limited to this.

[0017] 1.1. Server Figure 2 shows a diagram illustrating the configuration of the server 100 in this embodiment. As shown in Figure 2, the server 100 includes, for example, a processor 110, a communication interface 120, an input / output interface 130, memory 140, storage 150, and one or more buses 160 for interconnecting these components. The server 100 may be, for example, a desktop, laptop, or other computer. The server 100 is a general-purpose computer and may consist of a single computer or multiple computers located on a network N.

[0018] The processor 110 processes the code contained in the program stored in the storage 150. Alternatively, it executes a process, function, or method to be implemented by an instruction. As shown in Figure 2, The processor 110 of this embodiment includes a transmit / receive unit 111, a prediction unit 112, and a display instruction unit 1 It may function as 13.

[0019] The transmitting / receiving unit 111 may function, for example, as a transmitting unit that transmits various information to other devices such as a user terminal 200 via the communication interface 120 and network N, or as a receiving unit that receives various information from other devices such as the user terminal 200 or an external server 300. Hereinafter, the transmitting and receiving units will not be specifically distinguished and will be referred to as the transmitting / receiving unit 111.

[0020] For example, the transmitting / receiving unit 111 may function as a receiving unit that accepts input or changes to lifestyle variables from the user terminal 200, or as a transmitting unit that transmits information about the user's predicted risk of disease to the user terminal 200.

[0021] Figure 4A shows an example of the input screen D1 for lifestyle variables. As shown in Figure 4A, lifestyle variables may include the user's age, gender, information about body size, smoking, and alcohol consumption. Information about body size may include height, weight, BMI, etc. In addition to the above, lifestyle variables may also include information about exercise frequency and intensity, sleep, and diet.

[0022] As shown in Figure 4A, there are no particular restrictions on how lifestyle variables are entered. For example, information that can be quantified, such as information about body size, may be entered using sliders or similar methods. Information about smoking and drinking may be entered by selecting qualitative indicators (e.g., No, Light, Medium, Heavy).

[0023] The transmitting / receiving unit 111 may record the lifestyle variables received as described above in the storage 150. The storage 150 may also store genetic information, lifestyle variables, etc., associated with a user ID that uniquely identifies the user.

[0024] Lifestyle variables include the user's physiological information, the user's lifestyle information, and information about the user's and their close relatives' medical history. The user's physiological information may be obtained from user information that the user has entered in advance. The user's lifestyle information may be obtained from information entered by the user as a questionnaire via terminal 200. Furthermore, the physiological information and lifestyle information may be changed for each disease.

[0025] Physiological information may include age, sex, body size, vital signs, or information from blood tests. Vital signs include blood pressure, etc. Blood tests are generally not limited to values ​​detectable by blood tests, but examples include LP(a) (lipoprotein(a)), apolipoprotein B (ApoB), blood glucose, triglycerides, and total cholesterol.

[0026] Lifestyle information includes information on eating habits, exercise habits, sleep habits, smoking or passive smoking, and alcohol consumption. Eating habits may also include information on salt intake, etc.

[0027] The prediction unit 112 predicts the user's risk of disease based on genetic information and lifestyle variables. In this process, the prediction unit 112 can obtain information about the user's genetic information and lifestyle variables recorded in the storage 150 and perform risk prediction processing. The prediction unit 112 may also update the risk based on changes in lifestyle variables received by the transmission / reception unit 111.

[0028] An example of a risk assessment formula used by the prediction unit 112 is shown below. This allows the system to calculate a comprehensive risk score for each disease by weighting and summing the genetic risk of each SNP held by the user, as well as lifestyle variables. Note that each coefficient in this formula may differ for each disease being assessed.

number

[0029] The risk assessment performed by the prediction unit 112 is not particularly limited, but may be performed in two stages, for example. For example, in the first stage, the prediction unit 112 calculates a base risk value based on non-lifestyle variables such as age, sex, and genetic information. Then, in the second stage, the prediction unit 112 may perform a risk prediction based on genetic information and lifestyle variables by correcting the base risk value calculated above with lifestyle variables such as information on physique, information on smoking, and information on alcohol consumption.

[0030] The diseases for which the prediction unit 112 predicts risk are not particularly limited, but examples include at least one of the following: myocardial infarction, stroke, type 2 diabetes, hypertension, cirrhosis due to hepatitis C, chronic kidney disease, colorectal cancer, lung cancer, and stomach cancer.

[0031] The display instruction unit 113 may instruct the prediction unit 112 to display the predicted disease risk on the display device of the user terminal 200. Figure 4B shows an example screen D2 showing the risk prediction results. As shown in Figure 4B, the risk for each disease may be shown as 0-100%, or as any level such as 3 or 5 stages.

[0032] Furthermore, the display instruction unit 113 may instruct the display device of the user terminal 200 to display the future risk over time, along with the risk at the user's age. Figure 4C shows an example screen D3 showing the predicted risk results for each disease. For example, by tapping the area of ​​each disease shown in example screen D2, the user may transition to screen D3, which shows detailed predicted results for the risk of the tapped disease. As shown in Figure 4C, in example screen D3, with age on the horizontal axis, the risk of the disease at the current age may be shown along with the change in risk before and after age, assuming that the current lifestyle variables are maintained, using curves or the like. This allows the user to recognize the future or past risk when maintaining their current lifestyle and to improve their lifestyle as needed.

[0033] Furthermore, as shown in Figure 4C, screen example D3 may display selection buttons that allow users to change lifestyle variables. When a selection button is tapped, its display changes from other buttons; for example, its color may change. This allows users to visually recognize the currently selected lifestyle variable. Initially, the selection button corresponding to the lifestyle variable entered on screen D1 may be displayed selected.

[0034] For example, when the BMI changes from Underweight to Healthy range due to an operation on a selection button displayed on the user terminal 200, the transmitting / receiving unit 111 receives the change in lifestyle variables, and the prediction unit 112 updates the risk based on the change in lifestyle variables received by the transmitting / receiving unit 111. Then, as shown in the example screen D4 in Figure 4D, the display instruction unit 113 may instruct the transmitting / receiving unit 111 to display the risk before the update and the risk after the update in a comparative manner when it receives the change in lifestyle variables. This allows the user to recognize the future or past risks of changing their lifestyle and to improve their lifestyle as needed.

[0035] Furthermore, when the user taps the genetic risk button, the display instruction unit 113 may instruct it to display information regarding the risk of disease based on the user's genetic information, in other words, the risk of disease when only genetic information is considered and lifestyle variables are not taken into account, as shown in screen example D5 in Figure 5E. This allows the user to accurately understand their own genetic risk without considering lifestyle, and also to understand the extent to which lifestyle affects disease risk by comparing it with screen examples D3 and D4.

[0036] Processor 110 is, but is not limited to, one or more central processing units (CPUs), Microprocessor (MPU, Graphics Processing Unit (GPU), Processor Core, Multiprocessor) Processors, application-specific integrated circuits (ASICs), application-specific integrated circuits (FPGAs), etc. This includes integrated circuit (IC) chips, LS, and dedicated circuits as disclosed in each embodiment. Each of these processes, functions, or methods may be implemented.

[0037] The communication interface 120 transmits and receives various types of data with other devices via a network. This communication may be performed via wired or wireless connection, and any communication protocol may be used as long as communication between the devices is possible. For example, the communication interface 120 may be implemented as hardware such as a network adapter, various types of communication software, or a combination thereof.

[0038] The input / output interface 130 includes an input device for inputting various operations to the server 100, and an output device for outputting processing results processed by the server 100. For example, the input / output interface 130 may include information input devices such as a keyboard, mouse, and touch panel, and information output devices such as a display device 131. The server 100 may accept predetermined inputs or perform predetermined outputs by connecting an external input / output interface 130.

[0039] Memory 140 temporarily stores programs loaded from storage 150, A workspace is provided to the processor 110. The memory 140 is used by the processor 110. Various data generated while the program is running are also temporarily stored here. Memory 1 40 is, for example, DRAM, SRAM, DDR RAM or other random access solid-state memory. This can be high-speed random access memory such as a storage device, and these can be combined That's good too.

[0040] The storage 150 stores programs, various functional units, and various data. The storage 150 may also store information necessary for the above predictions, such as the user's genetic information and lifestyle variables.

[0041] Furthermore, the storage 150 may be, for example, one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices, or a combination thereof. Another example of the storage 150 is one or more storage devices installed remotely from the processor 110.

[0042] 1.2. User Terminals Figure 3 shows a diagram illustrating the configuration of the user terminal 200 in this embodiment. As shown in Figure 3, The user terminal 200 includes, for example, a processor 210, a communication interface 220, and input / output Power interface 230, memory 240, storage 250, and their components It includes one or more buses 260 for interconnecting them. User terminals 200 are, for example, , smartphones, mobile phones, desktops, laptops, tablets, handhelds This may also include computer devices, wearable devices, etc.

[0043] Processor 210 processes the code contained in the program stored in storage 250. Alternatively, it executes a process, function, or method to be implemented by an instruction. As shown in Figure 3, The processor 210 in this embodiment functions as a transmitting / receiving unit 211 and a display control unit 212. That's fine.

[0044] The transmitting / receiving unit 211 may function, for example, as a transmitting unit that transmits various information to other devices such as a server 100 via the communication interface 220 and network N, or as a receiving unit that receives various information from other devices. Hereinafter, the transmitting and receiving units will not be specifically distinguished and will be referred to as the transmitting / receiving unit 211.

[0045] The display control unit 212 provides the display device 231 with a screen for accepting user input, and a user input screen. You may also control the display of a screen that shows the processing results for the information entered by the user.

[0046] For example, in one embodiment, the display control unit 212 communicates to the display instruction unit 113 of the server 100. Therefore, various information received from the server 100 may be displayed on the display device (for example). (See Figures 4A-4E).

[0047] Furthermore, the configuration of the processor 210, communication interface 220, input / output interface 230, memory 240, storage 250, and bus 260 may be the same as that of the processor 110, communication interface 120, input / output interface 130, memory 140, storage 150, and bus 160.

[0048] 1.3. Operation Processing Referring to Figure 5, the processing of the information processing apparatus according to this embodiment and the details of each functional unit will be described. Figure 5 shows an example of a sequence diagram showing the processing performed by System 1 in this embodiment. Note that the processing procedure described below is merely an example, and each process may be modified as much as possible within the scope of the technical concept of this disclosure, and steps may be omitted, replaced, or added as appropriate.

[0049] In step S1, the user terminal 200 transmits lifestyle variables to the server 100 in response to user operations, and the server 100's transmitting / receiving unit 111 receives them. More specifically, information regarding physiological information and the medical history of the user and their close relatives may be registered in advance. In addition, information regarding the user's lifestyle may be registered in advance or accepted during the risk assessment.

[0050] Furthermore, the display instruction unit 113 may dynamically generate input forms for necessary lifestyle variables according to the user's basic information (e.g., age, gender) and selected disease category, and control their display on the terminal. For example, the number of questions, the number of choices, or the order of the questions may be variable according to user attributes and disease.

[0051] The transmitting / receiving unit 111 may record the information received from the user in the storage 150 as user information. Regarding genetic information, the results of analyzing the genetic sample received from the user may also be recorded in the storage 150 as user information.

[0052] If there is missing information in some of the physiological information, the transmitting / receiving unit 111 may perform a missing information storage process based on other entered physiological information. For example, if BMI is not entered, it may be stored based on weight and height. In addition, non-numeric information recorded as user information in storage 150 may be normalized or digitized.

[0053] In step S2, the prediction unit 112 of the server 100 predicts the risk of disease according to the received lifestyle variables and genetic information. At this time, depending on the disease selected by the user, a prediction model or algorithm may be selected, and the risk assessment of the disease may be performed using the selected prediction model or algorithm.

[0054] Then, in steps S3 and S4, the display instruction unit 113 of the server 100 transmits the risk result via the transmission / reception unit 111 and controls its display on the display device of the user terminal 200. At this time, the risk may be displayed in different colors according to the risk classification such as high, medium, or low, or according to the percentage. Furthermore, as shown in Figure 4C, the risk trend with future age may be displayed as a risk curve.

[0055] Furthermore, in step S5, the user terminal 200 transmits changes to lifestyle variables to the server 100 in response to user operations, and the server 100's transmitting / receiving unit 111 receives them.

[0056] In step S6, the prediction unit 112 of the server 100 predicts the risk of disease based on the received modified lifestyle variables and genetic information. In step S7, the display instruction unit 113 of the server 100 transmits the modified risk result via the transmission / reception unit 111. Then, in step S8, the display device of the user terminal 200 displays and controls the changes in risk before and after the modification. This visualizes past, present, and future risks, allowing users to simulate the results of actions such as lifestyle improvements.

[0057] 1.4. Variations Conventional health risk prediction models typically divide age groups into cohorts (age groups) with a fixed age range for risk assessment. However, this method presents a challenge: risk values ​​change discontinuously at cohort boundaries (for example, where age changes from 30 to 31), making it difficult to smoothly represent risk changes associated with increasing age. Furthermore, if the age for which risk can be calculated is limited to integer values, it is difficult to continuously simulate the risk progression from the user's current age to their future age, leaving room for improvement in prediction accuracy and the information provided to users. Against this backdrop, there is a need to realize continuous and highly accurate risk prediction that is appropriate for each age group.

[0058] 1.4.1. Base Risk Calculation Process (Base Algorithm) The prediction unit 112 calculates basic risk values ​​for each disease based on demographic factors such as the user's age and gender, as well as genetic risk, and generates a baseline risk curve. This risk calculation process, performed in step S2 of Figure 5, is represented by the following formula. The baseline risk obtained by formula 1 is a curve showing the basic risk progression with respect to age before correction for lifestyle factors.

number

[0059] 1.4.2. Midpoint Interpolation (Midpoint Formula) Next, the prediction unit 112 performs midpoint interpolation on the risk data for each age interval included in the baseline risk curve. Specifically, it uses the risk values ​​(y1, y2) of the upper age limit (x2) and lower age limit (x1) in each age interval (x1, x2) to determine the risk value M at the intermediate age. By using the midpoint risk value obtained in this way as a representative value for each age interval, the variability of data points is suppressed, and the change in the risk curve can be approximated smoothly.

number

[0060] 1.4.3. Polynomial Fitting For the risk data points after midpoint interpolation, the prediction unit 112 performs curve approximation using polynomial fitting. In this process, discrete midpoint risk values ​​are modeled as continuous functions using a polynomial function of a predetermined degree of quadratic or quadratic order or higher, as expressed by the following equation. In the following equation, p(x) is the risk value at that age, and x is a variable representing age. C0, C1, ..., Cn are coefficients determined by the least squares method or the like. This process smoothly approximates the risk for each disease as a function of age, and can represent changes in risk even with small changes in age.

number

[0061] 1.4.4. Curve smoothing process (Chaikin algorithm) The prediction unit 112 may further perform a smoothing process on the initial risk curve obtained by polynomial fitting, such as the Chaikin algorithm. The Chaikin algorithm is an iterative process that reduces corners by interpolating new points between the vertices of the polylinearly approximated curve, and is a method for improving the smoothness of the curve. Through such a smoothing process, fine irregularities on the risk curve are removed, and a smooth risk transition with excellent visibility and consistency can be achieved.

[0062] 1.4.5. Continuous interpolation and risk calculation according to age Through the above processes—namely, base risk calculation, midpoint interpolation, polynomial fitting, and smoothing—it becomes possible to treat the user's age as a continuous quantity (real value) and generate a risk curve that changes smoothly for each age. Therefore, even for non-integer ages, such as "45.5 years old," the risk can be smoothly calculated and presented.

[0063] Furthermore, while conventionally the risk was considered the same for each specific age range, in this embodiment, the risk value changes sequentially according to the change in age, making it possible to smoothly model the transition of risk before and after a certain age.

[0064] Figure 6 shows a diagram of the risk curve obtained by the above process. In Figure 6, the horizontal axis represents age, and the vertical axis represents the risk value. The risk curve shown on the left in Figure 6 shows the risk change based on a conventional model divided into age cohort units, and has the characteristic of changing in a step-like manner as age increases. On the other hand, the risk curve shown on the right represents the risk transition obtained from the calculation of base risk to curve fitting and smoothing processing based on this embodiment. The solid line is the curve showing the base risk, and the dashed line is the predicted value after correcting the base risk while considering other lifestyle factors. According to this embodiment, as shown on the right in Figure 6, it is possible to calculate the disease risk according to the user's age smoothly and continuously, which can contribute to improving the accuracy of predictions and understanding of the user.

[0065] 2. Information Processing Method In one embodiment of the information processing method of the server 100 of this embodiment, the information processing device performs the steps of: receiving input or modification of lifestyle variables; predicting the user's risk of disease based on genetic information and the lifestyle variables; and updating the risk based on the received modification of the lifestyle variables.

[0066] The specific details of the method in this embodiment are described in the operation processing above, so a detailed explanation is omitted here.

[0067] 3. Program In one embodiment of the server 100 program of this embodiment, the information processing device is instructed to perform the following steps: receiving input or modification of lifestyle variables; predicting the user's risk of disease based on genetic information and the lifestyle variables; and updating the risk based on the received modification of the lifestyle variables.

[0068] The program may be recorded on a readable recording medium. The specific details of the processing performed by the program in this embodiment are described in the operation processing section above, so a detailed explanation is omitted here. [Explanation of symbols]

[0069] 1...System, 100...Server, 110...Processor, 111...Transmit / Receive Unit, 112...Prediction Unit, 113...Display / Instruction Unit, 120...Communication Interface, 130...Input / Output Interface, 131...Display Device, 140...Memory, 150...Storage, 160...Bus, 200...User Terminal, 210...Processor, 211...Transmit / Receive Unit, 212...Display Control Unit, 220...Communication Interface, 230...Input / Output Interface, 231...Display Device, 240...Memory, 250...Storage, 260...Bus, 300...External Server.

Claims

1. A receiving unit that accepts input or changes to lifestyle variables, It includes a prediction unit that predicts the user's risk of disease based on genetic information and the lifestyle variables, The prediction unit updates the risk based on the changes in the lifestyle variables received by the receiving unit. Information processing device.

2. The lifestyle variables include the user's physiological information, the user's lifestyle information, and information regarding the user's and their close relatives' medical history. The information processing apparatus according to claim 1.

3. The aforementioned physiological information includes age, sex, body size, vital signs, or information from blood tests. The information processing apparatus according to claim 1.

4. The aforementioned lifestyle information includes information on eating habits, exercise habits, sleep habits, smoking or passive smoking, and information on alcohol consumption. The information processing apparatus according to claim 1.

5. The aforementioned diseases include myocardial infarction, stroke, type 2 diabetes, hypertension, cirrhosis due to hepatitis C, chronic kidney disease, colorectal cancer, lung cancer, and stomach cancer. The information processing apparatus according to claim 1.

6. The receiving unit has a display instruction unit that, when it receives a change in the lifestyle variable, instructs the receiving unit to display the risk before the update and the risk after the update in a way that allows for comparison. The information processing apparatus according to claim 1.

7. The prediction unit predicts the risk associated with the user's age, as well as future risks as time progresses. The information processing apparatus according to claim 1.

8. In an information processing device, A step to accept input or changes to lifestyle variables, A step of predicting the user's risk of disease based on genetic information and the aforementioned lifestyle variables, The steps of updating the risk based on the changes in the lifestyle variables received, are performed. program.

9. Information processing device, A step to accept input or changes to lifestyle variables, A step of predicting the user's risk of disease based on genetic information and the aforementioned lifestyle variables, The steps of updating the risk based on the changes in the lifestyle variables received are performed. Information processing methods.

Images