Disease risk prediction system and disease risk prediction method

Abstract

To provide information related to a disease risk based on relevancy between dental medical examination data and a disease grasped on the basis of receipt data.SOLUTION: A disease risk prediction system acquires dental medical examination data including a result of a dental medical examination or a dental inquiry performed for a plurality of people and medical receipt data including a result of medical care for a plurality of people, on the basis of the dental medical examination data and the medical receipt data, generates a risk prediction model which sets contents of the dental medical examination data as an explanatory variable, and sets information related to a specific disease grasped from the medical receipt data as an objective variable, then applies to the risk prediction model, the contents of the dental medical examination data performed for a subject for whom a risk about the specific disease is predicted, as an explanatory variable, for acquiring an objective variable, then outputs information based on the acquired objective variable.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a disease risk prediction system and a disease risk prediction method. [Background technology]

[0002] In recent years, in Japan, the control of medical expenses, which are increasing year by year, has become an issue, and medical insurers are being asked to optimize medical expenses while maintaining the health of insured persons. Against this background, medical insurers are promoting insurance business that utilizes medical information databases, such as medical receipt data collected from medical institutions, as one of the measures based on data health plans.

[0003] As an example of the use of a medical information database, for example, Patent Document 1 describes a platform system configured for the purpose of generating various types of health management intervention support information to support health management intervention by medical insurers for insured persons. The platform system generates health management intervention support information based on an insurer database that records the insured person's medical information, at least one of an IoT weighing scale, a wearable device, and an information and communication terminal used by the insured person, and information transmitted from the medical information and at least one of the IoT weighing scale, the wearable device, and the information and communication terminal. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2018-116395 A Summary of the Invention [Problem to be solved by the invention]

[0005] Recently, from the viewpoint of clarifying the relationship between dental checkup data and specific diseases, many reports have been made on the relationship between periodontal disease and diabetes. For example, it has been reported that inflammation-related chemicals released from periodontal pockets into the body cause "insulin resistance," which makes insulin less effective, thereby increasing the risk of developing diabetes. Therefore, by statistically analyzing the relationship between dental checkup data and medical receipt data to predict disease risk and providing the information obtained from the prediction to medical insurers, medical professionals, insured persons, etc., it is believed that this will lead to an increase in the rate of dental checkup attendance and the prevention of dental diseases, leading to the extension of people's healthy life expectancy.

[0006] Here, the above-mentioned Patent Document 1 describes that health management intervention support information is generated based on an insurer database that records the insured person's medical information, and information acquired from an IoT weight scale, wearable device, and information communication device used by the insured person, and is provided to the insurer, and the insured person receives guidance and advice based on the health management intervention support information. However, the document does not describe performing an analysis focusing on the correlation between dental checkup data and specific diseases identified from medical receipt data.

[0007] The present invention has been made in consideration of the above background, and aims to provide a disease risk prediction system and a disease risk prediction method that are capable of providing information regarding disease risk based on the correlation between diseases identified from dental checkup data and medical prescription data. [Means for solving the problem]

[0008] One of the present inventions for achieving the above object is a disease risk prediction system, which is configured using an information processing device having a processor and a memory element, and acquires dental checkup data including information on the results of dental checkups or dental interviews conducted on a plurality of individuals, and medical receipt data including information on the results of medical treatment conducted on the plurality of individuals, and generates a risk prediction model based on the dental checkup data and the medical receipt data, which is a model in which the content of the dental checkup data is an explanatory variable and information on a specific disease grasped from the medical receipt data is an objective variable, and acquires the objective variable by applying the content of the dental checkup data conducted on a subject whose risk for the specific disease is to be predicted as an explanatory variable to the risk prediction model, and outputs information based on the acquired objective variable. The risk prediction model is a statistical model generated by multiple regression analysis, and the explanatory variables used in the risk prediction model are selected based on a t-value or a confidence interval obtained by the multiple regression analysis. .

[0009] Problems, configurations and effects other than those described above will become apparent from the following description of the preferred embodiment of the invention. Effect of the Invention

[0010] According to the present invention, it is possible to provide information on disease risk based on the correlation between diseases identified from dental checkup data and medical receipt data, thereby enabling the extension of people's healthy life expectancy. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing a schematic configuration of a disease risk prediction system. [Diagram 2] 2 is an example of a hardware configuration of an information processing device constituting the disease risk prediction system. [Diagram 3] FIG. 2 is a diagram showing main functions of a disease risk prediction device. [Figure 4] 1 is an example of a table of dental checkup data. [Diagram 5] 1 is an example of a table of medical receipt data. [Figure 6]13 is an example of a specific disease master table. [Figure 7] 13 is an example of a medical treatment point master table. [Figure 8] 13 is an example of a table of dental checkup numerical data. [Figure 9] 13 is an example of a table of specific disease score data. [Figure 10] FIG. 2 is a diagram illustrating main functions of an administrator device. [Figure 11] FIG. 2 is a diagram showing main functions of a user device. [Figure 12] 13 is a flowchart illustrating a risk prediction model generation process. [Figure 13] 13 is a flowchart illustrating a dental checkup data digitization process. [Figure 14] 13 is a flowchart illustrating a specific disease point data generation process. [Figure 15] 1 is an example of explanatory variables and a response variable used in multiple regression analysis. [Figure 16] 13 is an example of a result of a multiple regression analysis. [Figure 17] 13 is a flowchart illustrating a risk prediction process. [Figure 18] 13 is an example of a disease risk presentation screen. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. The present invention can be implemented in various other forms. The following description and drawings are merely examples for explaining the present invention, and are omitted and simplified as appropriate for clarity of explanation. Each component described below may be singular or plural unless otherwise specified.

[0013] In the following description, various types of information may be described using expressions such as "information," "data," "table," and the like, but the various types of information may be expressed using data structures other than these. When describing identification information, expressions such as "identifier" and "ID" are used, but these are interchangeable.

[0014] In the following description, the letter "S" before a reference symbol indicates a processing step. In the following description, the same reference symbols may be used for identical or similar components, and duplicate descriptions may be omitted.

[0015] FIG. 1 shows a schematic configuration of an information processing system (hereinafter referred to as "disease risk prediction system 1") described as one embodiment of the present invention. As shown in the figure, disease risk prediction system 1 includes a disease risk prediction device 100, an administrator device 200, and a user device 300. All of these are configured using information processing devices (computers), and are connected to each other via a communication network 5 in a state in which they can communicate with each other bidirectionally. The communication network 5 is a communication infrastructure for realizing wired or wireless communication between the information processing devices, and may be, for example, a LAN (Local Area Network), a WAN (Wide Area Network), or the like. ), the Internet, various public communication networks, dedicated lines, serial communication media (USB communication devices (USB: Universal Serial Bus), etc.), etc.

[0016] The disease risk prediction device 100 predicts the risk of a specific disease for an insured person or other subject based on the relationship with a specific disease identified from information obtained from dental checkups and dental interviews conducted at dental medical institutions, companies, government agencies, schools, and other organizations (hereinafter referred to as "dental checkup data") and medical receipt data obtained from medical treatments and health checkups conducted at medical institutions (excluding dental clinics) such as hospitals and clinics (hereinafter referred to as "received receipt data") or organizations.

[0017] Here, the specific disease is, for example, a lifestyle-related disease such as diabetes, hypertension, dyslipidemia, etc. The risk (hereinafter also referred to as "disease risk") is, for example, the risk that the subject will contract a specific disease in the future, the risk that the disease the subject currently suffers from will become severe, or the future degree of progression. The disease risk prediction device 100 provides the predicted disease risk to medical professionals (doctors, dentists, nurses, dental hygienists, etc.), medical insurers (organizations that collect insurance premiums and provide insurance benefits to operate the medical insurance business), insured persons, etc. (hereinafter, these persons are collectively referred to as "users"). In the following, an example will be described in which the specific disease is diabetes.

[0018] The administrator device 200 is an information processing device operated by an administrator of the disease risk prediction device 100 when managing the administrator device 200. The above-mentioned management includes, for example, setting (registering, editing, deleting, etc.) various information for the disease risk prediction device 100, and monitoring and controlling the operation of the disease risk prediction device 100. The functions of the administrator device 200 may be realized as functions of the disease risk prediction device 100.

[0019] The user device 300 is an information processing device operated by a user when inputting and setting (registering, editing, deleting, etc.) various information required to receive information provided by the disease risk prediction device 100, viewing various information provided by the disease risk prediction device 100, etc. The user device 300 is used, for example, in medical institutions such as dental clinics and hospitals, organizations such as companies and government agencies, health checkup venues, etc. The subject of the operation of the user device 300 is not necessarily limited, but may be, for example, a medical professional such as a doctor or nurse, a medical insurer, or a person who gives health advice to a subject. The functions of the user device 300 may be realized as functions of the disease risk prediction device 100.

[0020] 2 shows an example of the hardware configuration of information processing devices (disease risk prediction device 100, administrator device 200, user device 300) constituting the disease risk prediction system 1. The illustrated information processing device 10 includes a processor 11, a main memory device 12, an auxiliary memory device 13, an input device 14, an output device 15, and a communication device 16. The information processing device 10 is, for example, a personal computer, a smartphone, a tablet, a server device, a general-purpose machine, etc.

[0021] Each information processing device (disease risk prediction device 100, administrator device 200, user device 300) may be realized, for example, using a plurality of information processing devices 10 communicably connected to each other.

[0022] In addition, the information processing device 10 may be realized, in whole or in part, by using virtual information processing resources provided by using virtualization technology, process space separation technology, or the like, such as a virtual server provided by a cloud system. In addition, all or in part of the functions provided by the information processing device 10 may be realized, for example, by a service provided by a cloud system via an API (Application Programming Interface) or the like. good.

[0023] Furthermore, all or part of the functions provided by the information processing device 10 may be realized using, for example, Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), or the like.

[0024] The processor 11 shown in the figure is, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), ASIC (Application Specific Integrated Circuit), It is composed of AI (Artificial Intelligence) chips, etc.

[0025] The main memory device 12 is a device for storing programs and data, and is, for example, a ROM (Read Only Memory). It is composed of memory elements such as non-volatile memory (NVRAM (Non Volatile RAM)), random access memory (RAM), and non-volatile memory (NVRAM).

[0026] The auxiliary storage device 13 is, for example, a solid state drive (SSD) or a hard disk drive. The auxiliary storage device 13 may be a hard disk, an optical storage device (CD (Compact Disc), DVD (Digital Versatile Disc), etc.), a storage system, a reading / writing device for a recording medium such as an IC card, an SD card, or an optical recording medium, a storage area of ​​a cloud server, etc. Programs and data can be read into the auxiliary storage device 13 via a recording medium reading device or a communication device 16. The programs and data stored (memorized) in the auxiliary storage device 13 are read into the main storage device 12 as needed.

[0027] In addition, all or part of the programs and data that realize the functions of the information processing device 10 may be stored in advance in the main memory device 12 or the auxiliary memory device 13, or, if necessary, may be read into the main memory device 12 or the auxiliary memory device 13 from a non-transitory recording medium or a non-transitory memory device provided in another device via a recording medium reading device or a communication device.

[0028] The input device 14 is an interface that accepts input from the outside, and is, for example, a keyboard, a mouse, a touch panel, a card reader, a pen-input tablet, a voice input device, or the like.

[0029] The output device 15 is an interface that outputs various information such as the process progress and the process result. The output device 15 is, for example, a display device (liquid crystal monitor, LCD (Liquid Crystal Display), graphic card, etc.) that visualizes the above-mentioned various information, a device that converts the above-mentioned various information into voice (voice output device (speaker, etc.)), and a device that converts the above-mentioned various information into text (printer, etc.). For example, the information processing device 10 may be configured to input and output information to and from other devices via the communication device 16.

[0030] The input device 14 and the output device 15 constitute a user interface that realizes interactive processing (receiving input of information, presenting information, etc.) with a user (a user or an administrator).

[0031] The communication device 16 is a device that realizes communication with other devices. The communication device 16 is a wired or wireless communication interface that realizes communication with other devices via the communication network 5, and is, for example, a NIC (Network Interface Card), a wireless communication module, a USB module, or the like.

[0032] The information processing device 10 may be implemented with, for example, an operating system, a file system, a DBMS (DataBase Management System) (relational database, NoSQL, etc.), a KVS (Key-Value Store), etc.

[0033] The various functions of the disease risk prediction device 100, the administrator device 200, and the user device 300 are realized by the processor 11 each of them has reading and executing a program stored in the main memory device 12, or by the hardware (FPGA, ASIC, AI chip, etc.) that constitutes each of them.

[0034] The disease risk prediction device 100, the administrator device 200, and the user device 300 store various types of information (data), for example, as tables in a database or files managed by a file system.

[0035] 3 shows the main functions of the disease risk prediction device 100. As shown in the figure, the disease risk prediction device 100 has, as its main functions, a storage unit 110, an information acquisition and management unit 120, a learning data generation unit 130, a model generation unit 140, and a risk prediction unit 150.

[0036] Of the above functions, the memory unit 110 manages (stores) each piece of information (data), including dental checkup data 101, prescription data 102, specific disease master 103, medical treatment fee master 104, dental checkup numerical data 105, specific disease fee data 106, multiple regression analysis results 107, risk prediction model 111, risk prediction results 112, and disease risk presentation screen 113.

[0037] Among the above information, the dental checkup data 101 includes checkup information and interview information acquired during dental checkups and dental interviews conducted by organizations such as dental medical institutions, companies, government offices, schools, etc. The above checkup information includes, for example, information such as the dentist's findings, the number of remaining teeth, and the presence or absence of periodontal pockets. The above interview information includes, for example, information such as dental concerns, tooth brushing habits, and pain. The dental checkup data 101 has been anonymized to protect personal information.

[0038] 4 shows an example of a table of the dental checkup data 101. The illustrated table corresponds to one of the subjects of a dental checkup or dental interview. That is, the dental checkup data 101 includes a plurality of the above tables for each subject.

[0039] As shown in the figure, the illustrated table is made up of multiple records that associate item names 1011 with answers 1012 to the items. The item names 1011 store the type of attributes of the subject ("insured serial number" (anonymized number on the insured certificate), "gender", and "age") and the content of questions asked of the subject during a dental checkup or dental interview (hereinafter referred to as "question content"). The answers 1012 store the subject's attributes and question content.

[0040] Returning to Fig. 3, the medical receipt data 102 is medical receipt data acquired during medical treatment (including examinations and diagnoses) performed at medical institutions such as hospitals and clinics (excluding dental clinics), and organizations such as companies, government offices, schools, etc. The medical receipt data 102 is acquired, for example, from a medical receipt information database (for example, NDB open data) provided by a national authority, etc. The medical receipt data 102 has been anonymized to protect personal information.

[0041] 5 shows an example of a table of the medical receipt data 102. The illustrated table corresponds to one of the subjects of medical treatment. That is, the medical receipt data 102 includes a plurality of the above tables for each subject.

[0042] As shown in the figure, the illustrated table is made up of multiple records that associate item names 1021 with values ​​1022 for the items. The item names 1021 store the type of attributes of the subject (insured person serial number) and the details of medical treatment given to the subject (such as "medical institution code," "year and month of medical treatment," "year and month of hospitalization," "determined points," "payer burden," "patient burden ratio," "disease code," etc.; hereinafter, these are collectively referred to as "medical treatment information"). The values ​​1022 store the attributes and medical treatment information of the subject.

[0043] Returning to Fig. 3, the specific disease master 103 includes a list of disease names (injury and illness names). The specific disease master 103 is referenced by the specific disease point data generating unit 132 of the learning data generating unit 130 for the purpose of extracting information on specific diseases from the medical receipt data 102 when generating the specific disease point data 106 described later. The specific disease master 103 is generated, for example, based on an injury and illness name master provided by a national authority or the like.

[0044] 6 shows an example of the specific disease master table 103. The specific disease master table 103 manages a list of names of diseases related to a specific disease (diabetes in this example).

[0045] Returning to Fig. 3, the medical treatment point master 104 includes information on points (medical treatment points) for each medical treatment (medical treatment name). The medical treatment point master 104 is used for the purpose of acquiring points for medical treatment performed for a specific disease extracted from the medical receipt data 102. The medical treatment point master 104 is generated, for example, based on a medical treatment procedure master provided by a national authority or the like.

[0046] Fig. 7 shows an example of a table of medical point master 104. Medical point master 104 manages points (medical point) for medical treatment and diagnosis for a specific disease (diabetes in this example). As shown in the figure, medical point master 104 manages information that associates medical procedure name 1041 with points 1042 for the medical procedure. The name of the medical procedure is stored in medical procedure name 1041. The points 1042 store the medical point for the medical procedure.

[0047] 3, the dental checkup quantified data 105 is information obtained by digitizing the content of the answer 1012 to the dental checkup data 101. The content of the dental checkup quantified data 105 is used as an explanatory variable by the model generating unit 140 when generating a risk prediction model.

[0048] 8 shows an example of a table of dental checkup numerical data 105. One of the illustrated tables corresponds to one of the subjects of a dental checkup or dental interview. That is, dental checkup numerical data 105 includes a plurality of the above tables for each subject.

[0049] As shown in the figure, the illustrated table includes information in which an item name 1051 corresponds to a value 1052. The item name 1051 stores the same content as the item name 1011 of the dental checkup data 101. The value 1052 stores a value obtained by digitizing the content of the answer 1012 of the dental checkup data 101. For example, when the item name 1011 is an attribute, a value obtained by digitizing the content of the attribute is stored in the value 1052, and when the item name 1011 is a question, a value obtained by digitizing the answer of the subject to the question according to the rank or type is stored in the value 1052. Note that an explanatory variable (item name 1051) that is not included in the dental checkup data 101 is added to the dental checkup digitized data 105 as necessary. In this example, the attribute of the subject, "age" is added to the item name 1051. In this way, by appropriately digitizing the dental checkup data 101, statistical processing such as multiple regression analysis, which will be described later, can be efficiently performed.

[0050] 3, the specific disease point data 106 is generated based on the medical receipt data 102, the specific disease master 103, and the medical treatment point master 104. The content of the specific disease point data 106 is used by the model generation unit 140 as a response variable when generating a risk prediction model.

[0051] 9 shows an example of a table of the specific disease score data 106. The illustrated table corresponds to one of the subjects of medical treatment or diagnosis. That is, the specific disease score data 106 includes a plurality of the above tables for each subject.

[0052] As shown in the figure, the illustrated table is made up of a number of records that associate item names 1061 with values ​​1062 for the items. In the item name 1061, "insured serial number," "disease name," and "total points" are stored. In the value 1062, content corresponding to the item name 1061 is set. For example, when the item name 1061 is "insured serial number," the insured serial number of the subject is stored in value 1062; when the item name 1061 is "disease name," the disease name of the subject (which may be a disease code) is stored in value 1062; and when the item name 1061 is "total points," the total points for each medical treatment performed for the subject is stored in value 1062.

[0053] 3, the multiple regression analysis result 107 includes information regarding the result of the multiple regression analysis performed by the model generating unit 140 when generating the risk prediction model 111. The above information will be described in detail later.

[0054] A risk prediction model 111 shown in the figure includes information (coefficients, etc., described later) about the risk prediction model generated by a model generating unit 140. The risk prediction model will be described in detail later.

[0055] The risk prediction result 112 shown in the figure includes information about the result (disease risk) predicted by the risk prediction unit 150 using the risk prediction model.

[0056] The disease risk presentation screen 113 shown in the figure includes screen information describing the contents of the risk prediction result 112, which is presented by the risk prediction unit 150 via the user device 300. The disease risk presentation screen 113 includes information and image data written in a markup language such as HTML (Hypertext Markup Language) or XML (Extensible Markup Language).

[0057] 3, the information acquisition and management unit 120 acquires and manages the dental checkup data 101, the medical receipt data 102, the specific disease master 103, and the medical treatment fee master 104, which are information necessary for generating a risk prediction model. The information acquisition and management unit 120 acquires the above information from a server device or an administrator device 200 on the Internet via the communication network 5, for example.

[0058] Of the functions shown in Fig. 3, the learning data generation unit 130 generates dental checkup quantified data 105 and specific disease point data 106 to be used as learning data in generating a risk prediction model. As shown in the figure, the learning data generation unit 130 includes a dental checkup data quantification unit 131 and a specific disease point data generation unit 132. Of these, the dental checkup data quantification unit 131 generates dental checkup quantified data 105 to be used in generating a risk prediction model, based on the dental checkup data 101. Meanwhile, the specific disease point data generation unit 132 generates the specific disease point data 106 based on the medical receipt data 102, the specific disease master 103, and the medical treatment point master 104.

[0059] Among the functions shown in Fig. 3, the model generation unit 140 generates a risk prediction model using learning data (dental checkup numerical data 105, specific disease score data 106). As shown in the figure, the model generation unit 140 includes a multiple regression analysis unit 141, an explanatory variable selection unit 142, and a model The multiple regression analysis unit 141 performs multiple regression analysis using the contents of the dental checkup numerical data 105 as explanatory variables and the contents of the specific disease score data 106 as objective variables. The explanatory variable selection unit 142 selects explanatory variables that have a large influence on the objective variables based on the results of the multiple regression analysis performed by the multiple regression analysis unit 141. The model registration unit 143 generates a risk prediction model using the explanatory variables selected by the explanatory variable selection unit 142, and registers the generated risk prediction model as the risk prediction model 111.

[0060] Among the functions shown in Figure 3, the risk prediction unit 150 uses the risk prediction model 111 generated by the model generation unit 140 to predict the disease risk of a specific disease for a subject such as an insured person, and generates a disease risk presentation screen 113 containing the predicted results and transmits it to the user device 300.

[0061] As shown in the figure, the risk prediction unit 150 includes an explanatory variable receiving unit 151, a model application unit 152, and a prediction result presentation unit 153. Of these, the explanatory variable receiving unit 151 receives an input of explanatory variables from a user via the user device 300. The model application unit 152 provides the explanatory variables received from the user to the risk prediction model 111 and obtains the result (objective variable). The prediction result presentation unit 153 generates a screen containing information based on the prediction result by the risk prediction model 111, and transmits it to the user device 300.

[0062] 10 shows main functions of the administrator device 200. As shown in the figure, the administrator device 200 includes a storage unit 210, a communication unit 220, and an information setting unit 230. The storage unit 210 stores information received from the administrator, information sent from the disease risk prediction device 100, information to be sent to the disease risk prediction device 100, and the like. The communication unit 220 communicates with the disease risk prediction device 100 via the communication network 5. The information setting unit 230 provides the administrator with a user interface for setting (registering, editing, deleting, searching, etc.) various pieces of information managed in the disease risk prediction device 100.

[0063] FIG. 11 shows main functions of the user device 300. As shown in the figure, the user device 300 includes a storage unit 310, a communication unit 320, and an explanatory variable receiving unit 330. Among these, the storage unit 310 stores information received from the user, information sent from the disease risk prediction device 100, information to be transmitted to the disease risk prediction device 100, etc. The communication unit 320 communicates with the disease risk prediction device 100 via the communication network 5. The explanatory variable receiving unit 330 provides the user with a user interface for inputting explanatory variables. The explanatory variable receiving unit 330 transmits the explanatory variables input by the user to the disease risk prediction device 100.

[0064] Next, the processing performed in the disease risk prediction system 1 will be described.

[0065] 12 is a flowchart explaining the process (hereinafter referred to as "risk prediction model generation process S1200") performed by the disease risk prediction device 100 when generating a risk prediction model. The risk prediction model generation process S1200 is executed, for example, when new information (e.g., dental checkup data 101 or medical receipt data 102) is provided by the information acquisition management unit 120, or when an execution instruction is given by the administrator via the administrator device 200. The risk prediction model generation process S1200 will be explained below with reference to this figure.

[0066] First, the information acquisition and management unit 120 of the disease risk prediction device 100 acquires the dental checkup data 101, the medical receipt data 102, the specific disease master 103, and the medical treatment fee master 104, which are information used to generate a risk prediction model (S1211).

[0067] Next, the learning data generating unit 130 of the disease risk prediction device 100 generates dental checkup numerical data 105 based on the dental checkup data 101 (hereinafter, referred to as “dental checkup data numerical data 105”). The process is referred to as "process S1212".

[0068] 13 is a flowchart explaining the dental checkup data digitization process S1212. The dental checkup data digitization process S1212 is performed for each table of the dental checkup data 101. As shown in the figure, in the dental checkup data digitization process S1212, the dental checkup data digitization unit 131 of the learning data generation unit 130 digitizes the contents of the answers 1012 of the dental checkup data 101 (S1311) and registers the digitized table in the dental checkup digitized data 105.

[0069] Returning to FIG. 12, the learning data generating unit 130 then performs a process of generating specific disease point data 106 based on the medical receipt data 102 (hereinafter referred to as "specific disease point data generating process S1213").

[0070] 14 is a flowchart for explaining the specific disease point data generation process S1213. Note that the specific disease point data generation process S1213 shown in the figure is performed for each table of the medical receipt data 102.

[0071] As shown in the figure, first, the specific disease point data generating unit 132 of the learning data generating unit 130 refers to the specific disease master 103 and extracts information on a specific disease from the medical receipt data 102 (S1411).

[0072] Next, the specific disease point data generating unit 132 identifies the medical procedure name 1041 corresponding to the extracted information from the medical procedure point master 104, and acquires the identified medical procedure name 1041 and the point 1042 corresponding to the medical procedure name (S1412).

[0073] Next, the specific disease point data generation unit 132 registers in the specific disease point data 106 a table consisting of records that associate the extracted medical procedure name 1041, the points 1042 corresponding to the medical procedure name, and the attributes of the prescription data 102 (insured serial number) (S1413).

[0074] Returning to FIG. 12, the model generating unit 140 then generates a risk prediction model based on the dental checkup numerical data 105 and the specific disease score data 106 (S1214 to S1216).

[0075] First, the multiple regression analysis unit 141 of the model generation unit 140 performs multiple regression analysis with the contents of the dental checkup quantified data 105 as explanatory variables and the contents of the specific disease score data 106 as objective variables (S1214). In this embodiment, the multiple regression analysis is performed using the following formula (hereinafter referred to as "Formula 1") as a regression formula. In Formula 1, X1, X2, X3, X4,... are explanatory variables, Y is an objective variable, and a0, a1, a2, a3, a4,... are coefficients (constants). The target period of the dental checkup quantified data 105 used as the explanatory variables and the target period of the specific disease score data 106 used as the objective variable are set to appropriate ranges depending on the contents of the risk to be predicted using the risk prediction model. [Number 1] (Formula 1) Y=a0+a1X1+a2X2+a3X3+a4X4+...

[0076] 15 shows an example of explanatory variable X (a value obtained from dental checkup quantification data 105) and objective variable Y (a value obtained from specific disease score data 106) to be applied to the above formula. The value in parentheses in the figure indicates the method of quantification. Since the score value 1062 of the specific disease score data 106 has a certain range (about 3 to 6 digits), in this embodiment, the score value 1062 is converted into a natural logarithm (ln) and substituted into the above formula.

[0077] In the above multiple regression analysis, the multiple regression analysis unit 141 uses the dental checkup quantification data of each subject. The content of 105 is substituted for the explanatory variable X in the above formula to obtain the objective variable Y, and the coefficients a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22, a23, a24, a25, a30, a31, a32, a33, a34, a35, a36, a37, a38, a39, a40, a41, a42, a43, a44, a45, a46, a47, a48, a49, a50, a51, a52, a53, a54, a55, a56, a57, a58, a59, a60, a61, a62 3、 a 4、 Determine the value of .

[0078] Returning to FIG. 12, next, the explanatory variable selection unit 142 of the model generation unit 140 selects explanatory variables that have a large influence on the objective variable (for example, explanatory variables that contribute significantly to the risk of aggravation of a specific disease, which is the objective variable) based on the results of the multiple regression analysis as explanatory variables to be used in constructing a risk prediction model (S1215).

[0079] An example of the result of the multiple regression analysis is shown in FIG. 16. A table 1600 in the figure shows information on explanatory variables (coefficients a i 1612 、 The table shows the explanatory variable scores (standard deviation 1613, t-value 1614, and confidence interval 1615). The t-value 1614 in the table is a value indicating the degree of influence (strength of correlation) of the explanatory variable on the score (medical treatment score) of the disease (diabetes). For example, if the t-value 1614 of the explanatory variable is 1.5 or more or -1.5 or less, the explanatory variable selection unit 142 determines that the correlation is strong and that the explanatory variable is worthy of selection. The confidence interval 1615 in the table indicates that the smaller the value, the lower the chance of coincidence. For example, if the value of the confidence interval 1615 of the explanatory variable is smaller than 0.05, the explanatory variable selection unit 142 determines that the chance of coincidence is low and that the explanatory variable is worthy of selection.

[0080] In this way, by constructing a risk prediction model by selecting explanatory variables that contribute greatly to the dependent variable based on the information obtained as a result of multiple regression analysis (t-value and confidence interval), it is possible to generate a simple and highly accurate risk prediction model, enabling efficient prediction of disease risk.

[0081] The degree of freedom adjusted coefficient of determination 1651 in FIG. 1650 indicates to what extent the risk prediction model based on table 1600 represents the population. In this example, the degree of freedom adjusted coefficient of determination 1651 is 0.8854, which indicates that the risk prediction model represents just under 9% of the population. Furthermore, the p-value 1652 of the F-test in table 1650 indicates that the risk prediction model cannot be rejected as being due to chance. In this example, the p-value 1652 of the F-test is close to 0, which indicates that the risk prediction model cannot be rejected as being due to chance.

[0082] 12, the model registration unit 143 of the model generation unit 140 then generates a risk prediction model based on the explanatory variables selected in S1215, and registers the generated risk prediction model as the risk prediction model 111 (S1216). Note that the risk prediction model 111 includes at least information indicating the selected explanatory variables and the coefficient a i (i=0, 1, 2, 3, . . . ) An example of the risk prediction model 111 registered by the model registration unit 143 is shown in the following formula (hereinafter, referred to as "Formula 2"). [Number 2] (Formula 2) Y=a0+a1X1+a2X2+a3X3+a4X4+a5X5

[0083] 17 is a flowchart explaining a process (hereinafter referred to as "risk prediction process S1700") in which the risk prediction section 150 of the disease risk prediction device 100 predicts a disease risk by applying explanatory variable values ​​received from a user via the user device 300 to the risk prediction model 111, and presents the predicted disease risk to the user. The risk prediction process S1700 is executed, for example, when a request for providing a disease risk is received from the user via the user device 300. The risk prediction process S1700 will be explained below with reference to this figure.

[0084] First, the explanatory variable receiving unit 151 of the risk prediction unit 150 receives explanatory variable values ​​(explanatory variable values ​​of a subject whose disease risk is to be predicted) from a user via the user device 300. Specifically, the explanatory variable receiving unit 151 generates a disease risk presentation screen 113 and transmits it to the user device 300, and the user device 300 presents the disease risk presentation screen 113 and receives the explanatory variable values ​​from the user (S1711).

[0085] FIG. 18 shows an example of the disease risk presentation screen 113. As shown in the figure, the disease risk presentation screen 113 shown by way of example has an explanatory variable setting field 1810, a target variable display field 1820, and a prediction execution button 1830. When a user sets explanatory variable values ​​(in this example, the values ​​of five explanatory variables) in the explanatory variable setting field 1810 and operates the prediction execution button 1830, the contents set in the explanatory variable setting field 1810 are transmitted to the disease risk prediction device 100. Note that a pull-down menu display button 1811 is provided in each explanatory variable setting field of the explanatory variable setting field 1810, and when a user operates the display button 1811, selectable values ​​for each explanatory variable are automatically displayed, and the user can easily set explanatory variables by selecting the displayed value by operating the mouse or the like.

[0086] 17, the model application unit 152 of the risk prediction unit 150 then applies the explanatory variable values ​​received from the user to the risk prediction model 111, and obtains the resulting value of the objective variable (disease risk) (S1712). For example, when the risk prediction model 111 is expressed by Equation 2, the model application unit 152 obtains the value of the objective variable Y by substituting the explanatory variable values ​​received from the user for the explanatory variables X1, X2, X3, X4, and X5 in the equation.

[0087] Next, the prediction result presentation unit 153 of the risk prediction unit 150 transmits the value of the acquired objective variable (disease risk) to the user device 300, which receives it and presents it to the user via the disease risk presentation screen 113 (S1713).

[0088] As shown in FIG. 18, the user device 300 displays the value of the objective variable (disease risk) received from the disease risk prediction device 100 in the objective variable display field 1820 of the disease risk presentation screen 113. In this example, the user device 300 displays a value (in this example, the objective variable value) indicating the risk of disease (diabetes) of the insured person (subject) in 5 years and 10 years in the objective variable display field 1820. In this example, the disease risk in 5 years and 10 years is displayed, and in this case, the model generation unit 140 generates, for example, learning data required to predict the disease risk in 5 years and 10 years, and generates a risk prediction model for each separately. In this example, two objective variables in 5 years and 10 years are presented, but, for example, a designation of how many years later the objective variable should be presented may be accepted from the user, and the value of the objective variable (disease risk) may be obtained using a risk prediction model corresponding to the designated time, and the obtained value of the objective variable may be presented to the user.

[0089] Returning to FIG. 17, the risk prediction unit 150 then judges whether the user has changed the value of the explanatory variable on the disease risk presentation screen 1800 (whether or not a re-execution instruction has been given) (S1714). If the user has changed the value of the explanatory variable (S1714: YES), the process returns to S1711, and the same process as above is repeated for the changed value of the explanatory variable. In this way, the user can change the value of the explanatory variable by a simple operation using the disease risk presentation screen 1800. Therefore, it is possible to check the prediction results of disease risk for various combinations of explanatory variable values, and for example, it is possible to check what factors affect future disease risk and how they affect it.

[0090] On the other hand, if the user has not changed the value of the explanatory variable (S1714: NO), the risk prediction unit 150 checks whether the user has operated the back button 1840 on the disease risk presentation screen 1800. It is determined whether or not an end instruction has been given (S1715). If the user has not operated the back button 1840 (S1715: NO), the process returns to S1714. If the user operates the back button 1840 (S1715: YES), control is transferred to the screen that called the disease risk presentation screen 1800, and the risk prediction process S1700 ends.

[0091] As described above, according to the disease risk prediction system of this embodiment, information on disease risk based on the association between dental checkup data and diseases identified from medical receipt data can be provided to medical insurers, medical professionals, insured persons, etc. Therefore, it is possible to promote an increase in the rate of dental checkup attendance and the prevention of dental diseases, which can lead to an extension of people's healthy life expectancy. As a result, it is expected to contribute to both maintaining the health of insured persons and optimizing medical expenses.

[0092] Although one embodiment of the present invention has been described above, it goes without saying that the present invention is not limited to the above embodiment and can be modified in various ways without departing from the gist of the present invention. For example, the above embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to an embodiment having all of the configurations described. In addition, it is possible to add, delete, or replace part of the configuration of the above embodiment with other configurations.

[0093] For example, in the above embodiments, the risk prediction model is configured using a linear regression model, but the risk prediction model may also be configured using, for example, other types of statistical models or machine learning models (e.g., DNN (Deep Neural Network)).

[0094] The above-mentioned configurations, functional units, processing units, processing means, etc. may be realized in part or in whole by hardware, for example, by designing them as integrated circuits. Also, the above-mentioned configurations, functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function. Information such as the programs, tables, and files that realize each function may be stored in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), an IC card, etc. It can be placed on a storage medium such as a notebook, SD card, or DVD.

[0095] The above-described layouts of the various functional units, processing units, and databases of each information processing device are merely examples. The layouts of the various functional units, processing units, and databases can be changed to optimal layouts in terms of the performance, processing efficiency, communication efficiency, and the like of the hardware and software that these devices are equipped with.

[0096] The configuration (schema, etc.) of the database that stores the various types of data described above can be flexibly changed from the viewpoints of efficient use of resources, improved processing efficiency, improved access efficiency, improved search efficiency, and the like. [Explanation of symbols]

[0097] 1 Disease risk prediction system, 5 Communication network, 10 Information processing device, 100 Disease risk prediction device, 101 Dental checkup data, 102 Receipt data, 103 Specific disease master, 104 Medical treatment point master, 105 Dental checkup quantification data, 106 Specific disease point data, 107 Multiple regression analysis result, 110 Memory unit, 111 Risk prediction model, 112 Risk prediction result, 113 Disease risk presentation screen, 120 Information acquisition management unit, 130 Learning data generation unit, 131 Dental checkup data quantification unit, 132 Specific disease score data generation unit, 140 Model generation unit, 141 Multiple regression analysis unit, 142 Explanatory variable selection unit, 143 Model registration unit, 150 risk prediction unit, 151 explanatory variable reception unit, 152 model application unit, 153 prediction result presentation unit, 200 administrator device, 300 user device, S1200 risk prediction model generation process, S1212 dental checkup data digitization process, S1213 specific disease score data generation process, S1700 risk prediction process, 1800 disease risk presentation screen

Claims

1. The information processing device includes a processor and a memory device. Dental examination data including information regarding the results of dental examinations or dental interviews conducted on a number of individuals; Medical receipt data including information regarding the results of medical treatment performed on the plurality of persons; Get A risk prediction model is generated based on the dental checkup data and the medical receipt data, the risk prediction model being a model in which the content of the dental checkup data is an explanatory variable and information on a specific disease grasped from the medical receipt data is an objective variable; The content of the dental checkup data obtained for the subject for whom the risk of the specific disease is to be predicted is applied as an explanatory variable to the risk prediction model to obtain the objective variable; Outputting information based on the acquired objective variables. The risk prediction model is a statistical model generated by multiple regression analysis; The explanatory variables to be used in the risk prediction model are selected based on a t-value or a confidence interval obtained by the multiple regression analysis. Disease risk prediction system.

2. The disease risk prediction system according to claim 1, The objective variable is information indicating a risk of contracting the specific disease in the future, or information indicating a risk of the specific disease becoming severe. Disease risk prediction system.

3. The disease risk prediction system according to claim 1, The explanatory variables include values ​​obtained by quantifying the results of dental checkups or dental interviews in the dental checkup data. Disease risk prediction system.

4. The disease risk prediction system according to claim 1, The information regarding the specific disease obtained from the medical receipt data includes information indicating the points of medical treatment performed for the specific disease. Disease risk prediction system.

5. The disease risk prediction system according to claim 1, presenting a screen having a setting field for receiving values ​​of the explanatory variables for the subject for whom the risk of the specific disease is to be predicted, and a presentation field for presenting information based on the objective variables obtained by applying the values ​​set in the setting field to the risk prediction model; Disease risk prediction system.

6. An information processing device having a processor and a memory element, Dental examination data including information regarding the results of dental examinations or dental interviews conducted on a number of individuals; Medical receipt data including information regarding the results of medical treatment performed on the plurality of persons; and obtaining A step of generating a risk prediction model based on the dental checkup data and the medical receipt data, the risk prediction model being a model in which the content of the dental checkup data is an explanatory variable and information on a specific disease grasped from the medical receipt data is an objective variable; A step of acquiring the objective variable by applying the contents of the dental checkup data obtained for the subject for whom the risk of the specific disease is to be predicted as an explanatory variable to the risk prediction model; outputting information based on the acquired objective variables; selecting the explanatory variables to be used in a risk prediction model, which is a statistical model generated by multiple regression analysis, based on a t-value or a confidence interval obtained by the multiple regression analysis; The disease risk prediction method further comprises:

7. The disease risk prediction method according to claim 6, The objective variable is information indicating a risk of contracting the specific disease in the future, or information indicating a risk of the specific disease becoming severe. Disease risk prediction methods.

8. The disease risk prediction method according to claim 6, The explanatory variables include values ​​obtained by quantifying the results of dental checkups or dental interviews in the dental checkup data. Disease risk prediction methods.

9. The disease risk prediction method according to claim 6, The information regarding the specific disease obtained from the medical receipt data includes information indicating the points of medical treatment performed for the specific disease. Disease risk prediction methods.

10. The disease risk prediction method according to claim 6, a step of the information processing device presenting a screen having a setting field for receiving values ​​of the explanatory variables for the subject for whom the risk of the specific disease is to be predicted, and a presentation field for presenting information based on the objective variables obtained by applying the values ​​set in the setting field to the risk prediction model; The disease risk prediction method further comprises:

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