JP7427451B2 - Information processing device, onset probability determination method, and onset probability determination program - Google Patents

Description

The present invention relates to an information processing device, an onset probability determination method, and an onset probability determination program.

Brain diseases such as cerebral infarction and brain tumors are diseases that are likely to recur. Furthermore, if the disease recurs, it tends to become more severe and often life-threatening. It is also known that rehabilitation immediately after the onset of symptoms can reduce the aftereffects. Therefore, it is important to detect the onset of brain diseases early.

Patent No. 4441603

However, with conventional techniques, there has been a problem in that it is difficult to detect the onset of brain diseases at an early stage. In other words, once a brain disease has developed, there is a high possibility that the brain disease will recur, but there has been a problem in that it is not easy to detect the onset of a brain disease early.

In order to solve the above-mentioned problems and achieve the objects, an information processing device of the present invention includes an acquisition unit that acquires vital data and meal data of a specific user, and the vital data and the meal data acquired by the acquisition unit. a determination unit that determines the probability that the specific user will develop a brain disease using a model that determines the probability that the user will develop a brain disease using dietary data as input data; and an output unit that outputs a probability that the specific user will develop a brain disease.

Further, the onset probability determination method of the present invention is an onset probability determination method executed by an information processing device, and includes an acquisition step of acquiring vital data and dietary data of a specific user, and a a determination step of determining the probability that the specific user will develop a brain disease using a model that determines the probability that the user will develop a brain disease using vital data and the dietary data as input data; and the determination step and an output step of outputting the probability that the specific user will develop a brain disease determined by.

Further, the onset probability determination program of the present invention includes an acquisition step of acquiring vital data and dietary data of a specific user, and the vital data and dietary data acquired in the acquisition step are input data, and the user a determining step of determining the probability that the specific user will develop a brain disease using a model that determines the probability of developing a disease; and a determining step that the specific user will develop a brain disease as determined by the determining step. The present invention is characterized by causing a computer to execute an output step of outputting the probability.

According to the present invention, it is possible to detect the onset of a brain disease at an early stage.

FIG. 1 is a block diagram showing a configuration example of an information processing apparatus according to the first embodiment. FIG. 2 is a diagram showing an example of data stored in the meal data storage section. FIG. 3 is a diagram illustrating an overview of the onset probability determination process performed by the information processing apparatus according to the first embodiment. FIG. 4 is a diagram showing a screen example of the onset probability displayed on the terminal device. FIG. 5 is a flowchart illustrating an example of the flow of processing in the information processing apparatus according to the first embodiment. FIG. 6 is a diagram showing a computer that executes the onset probability determination program.

Embodiments of an information processing device, an onset probability determination method, and an onset probability determination program according to the present application will be described in detail below with reference to the drawings. Note that the information processing device, the onset probability determination method, and the onset probability determination program according to the present application are not limited by this embodiment.

[First embodiment]
In the following embodiment, the configuration of the information processing device 10 according to the first embodiment and the processing flow of the information processing device 10 will be explained in order, and finally, the effects of the first embodiment will be explained.

[Configuration of information processing device]
First, the configuration of the information processing device 10 will be explained using FIG. 1. FIG. 1 is a block diagram showing a configuration example of an information processing apparatus according to the first embodiment. As shown in FIG. 1, the information processing device 10 is connected to a plurality of terminal devices 20A and 20B via a network 30. Note that the terminal device 20A and the terminal device 20B will be referred to as the terminal device 20 when they are explained without distinction.

Here, the terminal device 20A and the terminal device 20B are, for example, information processing devices such as a smartphone, a desktop PC, a tablet PC, a notebook PC, a mobile phone, and a PDA (Personal Digital Assistant).

Further, as shown in FIG. 1, the information processing device 10 includes a communication processing section 11, a control section 12, and a storage section 13. The information processing device 10 acquires vital data and dietary data of a plurality of users who have experienced the onset of brain diseases such as brain tumors and cerebral infarctions, as well as subsequent recurrence status of brain diseases, from a plurality of terminal devices 20. The data is used to train a model that determines the probability that a user will develop a brain disease.

The information processing device 10 also acquires daily vital data and dietary data of a specific user who has a history of developing a brain disease from the terminal device 20, and requests determination of the probability of developing a brain disease from the specific user. If the system accepts the specific user's vital data and dietary data as input data, the trained model is used to determine the probability of developing a brain disease from the specific user, and output to the terminal device 20. do. The processing of each unit included in the information processing device 10 will be described below.

The communication processing unit 11 controls communication regarding various information. For example, the communication processing unit 11 receives vital data and meal data of a user who owns the terminal device 20 from the terminal device 20.

The storage unit 13 stores data and programs necessary for various processing by the control unit 12. The storage unit 13 includes a meal data storage unit 13a, a user data storage unit 13b, and a model storage unit 13c. For example, the storage unit 13 is a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk.

The meal data storage unit 13a stores information regarding the nutritional value of meals. For example, the meal data storage unit 13a stores "protein," "energy," and "salt" for each meal menu, as illustrated in FIG. Note that the example in FIG. 2 is just an example, and in addition to protein, energy, and salt, information regarding other nutritional values such as sugar content may be stored as the nutritional value of the meal.

The user data storage unit 13b stores various data of the user who holds the terminal device 20. For example, the user data storage unit 13b stores vital data and meal data acquired by an acquisition unit 12c, which will be described later, for each user. The model storage unit 13c stores the model learned by the learning unit 12b.

The control unit 12 has an internal memory for storing programs that define various processing procedures and necessary data, and executes various processes using these, but the following are particularly closely related to the present invention: It has a collection section 12a, a learning section 12b, an acquisition section 12c, a determination section 12d, and an output section 12e. Here, the control unit 12 is an electronic circuit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit), or an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

The processes executed by each functional unit of the control unit 12 include a learning phase in which a model for determining the probability of developing a brain disease is learned, and a learning phase in which the learned model is used to determine the probability that the user will develop a brain disease. It is roughly divided into the onset probability determination phase. The collection unit 12a and the learning unit 12b in the control unit 12 are functional units that perform processing in the learning phase, and the acquisition unit 12c, determination unit 12d, and output unit 12e in the control unit 12 are functional units that perform processing in the onset probability determination phase. Department.

The information processing device 10 according to the first embodiment will be described as a device that performs both learning processing in the learning phase and onset probability determination processing in the onset probability determination phase, but is not limited to this. Only the onset probability determination process in the probability determination phase may be performed. In this case, the information processing device 10 determines the probability of developing a brain disease using a model learned by another device. Each functional unit in the control unit 12 will be explained below.

The collection unit 12a collects vital data and dietary data of a plurality of users, and disease information indicating whether each user has developed a brain disease. Specifically, the collection unit 12a collects vital data and dietary data of a plurality of users who have developed a brain disease, and also determines whether or not the brain disease of each user has recurred and when it has recurred. Collect as information.

For example, the collection unit 12a collects data on the user's blood pressure and heart rate over a predetermined period from the terminal device 20 as vital data. The collection unit 12a collects, for example, blood pressure and heart rate data measured by a wearable device worn by the user via the terminal device 20.

Further, for example, the collection unit 12a collects information on meals consumed by the user during a predetermined period from the terminal device 20 as meal data. Regarding the input of the menu of the meal that the user has taken, the user may input it manually using the terminal device 20, or the user may take an image of the meal using the terminal device 20 and send it to the information processing device 10. The meal menu may be specified from the image taken by the information processing device 10. Moreover, when the collection unit 12a specifies a meal menu from the meal data, the collection unit 12a may refer to the meal data storage unit 13a and acquire the nutritional value corresponding to the specified meal menu.

In addition, the collection unit 12a collects vital data, dietary data, and disease information, and also collects any one or more of information regarding brain waves, information regarding blood, information regarding activities, and audio information regarding speech of a plurality of users. You may collect more. For example, the collection unit 12a collects each user's blood data and brain wave data obtained from hospital tests and the like. Further, the collection unit 12a may acquire the user's exercise activity amount, water intake amount during exercise, and oxygen amount during exercise, which are input automatically or manually using the terminal device 20. Further, the collection unit 12a may collect the user's voice data input from the microphone of the terminal device 20, and obtain an evaluation value of whether or not the speech fluctuates and the rhythm is correct from the voice data.

The learning unit 12b uses the vital data, dietary data, and disease information collected by the collecting unit 12a to learn a model for determining the probability that a user will develop a brain disease. For example, the learning unit 12b uses the disease information collected by the collecting unit 12a (whether or not the brain disease has recurred and when it has recurred) as the correct label, and uses vital data and dietary data to develop a model through supervised learning. learn.

For example, the learning unit 12b performs model learning using the user's blood pressure and heart rate data over a predetermined period as vital data. Further, for example, the learning unit 12b performs model learning using nutritional values such as protein, energy, and salt corresponding to the meal menu as meal data.

In addition, the learning unit 12b uses one or more of the vital data and dietary data collected by the collecting unit 12a, as well as disease information, brain wave information, blood information, activity information, and voice information. , a model that determines the probability that a user will develop a brain disease may be learned. For example, the learning unit 12b uses blood data, brain wave data, the user's activity level due to exercise, water intake during exercise, oxygen amount during exercise, evaluation value of whether speech fluctuates and rhythm is correct, etc. , perform model learning. Note that as for the learning method, any existing method may be applied.

After learning the model, the learning unit 12b stores the learned model in the model storage unit 13c as a learned model.

The acquisition unit 12c acquires vital data and meal data of a specific user. For example, the acquisition unit 12c acquires vital data and dietary data of a user who has developed a brain disease and is subject to the probability determination, and stores the acquired data in the user data storage unit 13b.

For example, the acquisition unit 12c acquires the user's blood pressure and heart rate data for a predetermined period from the terminal device 20 as vital data. The acquisition unit 12c acquires, for example, blood pressure and heart rate data measured by a wearable device worn by the user via the terminal device 20.

Further, for example, the acquisition unit 12c collects information on meals consumed by the user during a predetermined period from the terminal device 20 as meal data. Regarding the input of the menu of the meal that the user has taken, the user may input it manually using the terminal device 20, or the user may take an image of the meal using the terminal device 20 and send it to the information processing device 10. The meal menu may be specified from the image taken by the information processing device 10. Moreover, when the acquisition unit 12c specifies a meal menu from the meal data, the acquisition unit 12c may refer to the meal data storage unit 13a and acquire the nutritional value corresponding to the specified meal menu.

In addition, the acquisition unit 12c collects vital data, dietary data, and disease information, and further acquires one or more of the user's brain wave information, blood information, activity information, and speech information. You may. For example, the acquisition unit 12c acquires each user's blood data and brain wave data obtained from a hospital examination or the like. Further, the acquisition unit 12c may acquire the user's exercise activity amount, water intake amount during exercise, and oxygen amount during exercise, which are input automatically or manually using the terminal device 20. Further, the acquisition unit 12c may collect the user's voice data input from the microphone of the terminal device 20, and acquire from the voice data an evaluation value of whether or not the speech fluctuates and the rhythm is correct.

The determination unit 12d uses the vital data and dietary data acquired by the acquisition unit 12c as input data to determine the probability that a specific user will develop a brain disease using a model that determines the probability that a user will develop a brain disease. Determine. Specifically, the determination unit 12d reads out the learned model learned by the learning unit 12b from the model storage unit 13c, inputs vital data and meal data to the read out learned model, and calculates the onset probability.

For example, the determination unit 12d inputs the user's blood pressure and heart rate data for a predetermined period into the learned model as vital data. Further, the determination unit 12d inputs nutritional values such as protein, energy, and salt corresponding to the meal menu to the learned model as meal data.

In addition, the determination unit 12d has already learned one or more of the following: disease information, brain wave information, blood information, activity information, and voice information, in addition to the vital data and dietary data collected by the collection unit 12a. It can also be input into the model.

The output unit 12e outputs the probability that a specific user will develop a brain disease determined by the determination unit 12d to the terminal device 20. Further, the output unit 12e may output the probability of developing a brain disease of the user and also output a lifestyle improvement menu determined based on the dietary data. For example, the output unit 12e analyzes nutritional deficiencies, lack of exercise, etc. based on the nutritional value bias of the user based on the dietary data, and outputs a lifestyle improvement menu based on the analysis results. Note that as for the analysis method, any existing analysis method may be applied.

Here, an outline of the onset probability determination process performed by the information processing apparatus will be explained using FIG. 3. FIG. 3 is a diagram illustrating an overview of the onset probability determination process performed by the information processing apparatus according to the first embodiment. As illustrated in FIG. 3, a user who has a terminal device (user terminal) 20 operates the terminal device 20 to transmit various user data such as meal data and vital data to the information processing device 10, and Request the probability of disease onset. The information processing device 10 inputs various data such as meal data and vital data acquired from the terminal device 20 into a learning model, and calculates the user's probability of onset. The information processing device 10 then outputs the user's brain disease onset probability and lifestyle improvement menu to the terminal device 20.

Here, an example of a screen showing the onset probability displayed on the terminal device 20 will be explained using FIG. 4. FIG. 4 is a diagram showing a screen example of the onset probability displayed on the terminal device. As illustrated in FIG. 4, the terminal device 20 displays a graph screen showing changes in the probability of onset. Note that the eight-spot probability may be displayed in any manner and is not limited to this.

[Processing procedure of information processing device]
Next, an example of a processing procedure by the information processing apparatus 10 according to the first embodiment will be described using FIG. 5. FIG. 5 is a flowchart illustrating an example of the flow of processing in the information processing apparatus according to the first embodiment.

As illustrated in FIG. 5, when the acquisition unit 12c of the information processing device 10 acquires various data of the user such as meal data and vital data acquired from the terminal device 20 (Yes in step S101), the acquisition unit 12c of the information processing device 10 transfers the various data of the user to the user. The data is stored in the data storage section 13b (step S102).

Then, the determining unit 12d reads out the trained model learned by the learning unit 12b from the model storage unit 13c, inputs vital data and dietary data to the read out trained model (step S103), and calculates the onset probability ( Step S104). After that, the output unit 12e outputs the onset probability calculated by the determination unit 12d to the terminal device 20 (step S105).

(Effects of the first embodiment)
The information processing device 10 according to the first embodiment acquires vital data and dietary data of a specific user, and uses the acquired vital data and dietary data as input data to determine the probability that the user will develop a brain disease. The model is used to determine the probability that a specific user will develop a brain disease, and the determined probability that the specific user will develop a brain disease is output. For this reason, the information processing device 10 periodically acquires the user's vital data and dietary data, and determines the probability that the user will develop a brain disease using a model that determines the probability that the user will develop a brain disease. Therefore, it is possible to detect the onset of brain diseases early.

For example, the information processing device 10 acquires vital data and dietary data of a plurality of users who have experienced the onset of brain diseases such as brain tumors and cerebral infarctions, as well as subsequent recurrence status of brain diseases, from a plurality of terminal devices 20. The acquired data is used to learn a model that determines the probability that a user will develop a brain disease again. For this reason, the information processing device 10 uses vital data and dietary data of multiple users who have experience of developing a brain disease to accurately develop a model for determining the probability that a user who has developed a brain disease will recur. It is possible to learn well.

Furthermore, the information processing device 10 acquires daily vital data and dietary data of a specific user who has experienced the onset of symptoms from the terminal device 20. When the information processing device 10 receives a request for determining the probability of developing a brain disease from the specific user, the information processing device 10 uses the learned model using the specific user's vital data and dietary data as input data. Then, the probability of recurrence of the brain disease from the specific user is determined and outputted to the terminal device 20. Therefore, the information processing device 10 can detect recurrence of brain disease at an early stage.

(System configuration, etc.)
Furthermore, each component of each device shown in the drawings is functionally conceptual, and does not necessarily need to be physically configured as shown in the drawings. In other words, the specific form of distributing and integrating each device is not limited to what is shown in the diagram, and all or part of the devices can be functionally or physically distributed or integrated in arbitrary units depending on various loads and usage conditions. Can be integrated and configured. Furthermore, all or any part of each processing function performed by each device may be realized by a CPU and a program that is analyzed and executed by the CPU, or may be realized as hardware using wired logic.

Furthermore, among the processes described in this embodiment, all or part of the processes described as being performed automatically can be performed manually, or the processes described as being performed manually can be performed manually. It is also possible to perform all or part of this automatically using a known method. In addition, information including processing procedures, control procedures, specific names, and various data and parameters shown in the above documents and drawings may be changed arbitrarily, unless otherwise specified.

(program)
Further, it is also possible to create a program in which the processing executed by the information processing apparatus described in the above embodiment is written in a language executable by a computer. For example, it is also possible to create an onset probability determination program that describes the processing executed by the information processing device 10 according to the embodiment in a computer-executable language. In this case, when the computer executes the onset probability determination program, the same effects as in the above embodiment can be obtained. Further, by recording the onset probability determination program on a computer-readable recording medium, and having the computer read and execute the onset probability determination program recorded on the recording medium, the same processing as in the above embodiment can be realized. It's okay.

FIG. 6 is a diagram showing a computer that executes the onset probability determination program. As illustrated in FIG. 6, the computer 1000 includes, for example, a memory 1010, a CPU 1020, a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. However, each of these parts is connected by a bus 1080.

The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012, as illustrated in FIG. The ROM 1011 stores, for example, a boot program such as BIOS (Basic Input Output System). Hard disk drive interface 1030 is connected to hard disk drive 1090, as illustrated in FIG. Disk drive interface 1040 is connected to disk drive 1100, as illustrated in FIG. For example, a removable storage medium such as a magnetic disk or an optical disk is inserted into disk drive 1100. The serial port interface 1050 is connected to, for example, a mouse 1110 and a keyboard 1120, as illustrated in FIG. Video adapter 1060 is connected to display 1130, for example, as illustrated in FIG.

Here, as illustrated in FIG. 6, the hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. That is, the above-mentioned onset probability determination program is stored, for example, in the hard disk drive 1090 as a program module in which commands to be executed by the computer 1000 are written.

Further, the various data described in the above embodiments are stored as program data in, for example, the memory 1010 or the hard disk drive 1090. Then, the CPU 1020 reads out the program module 1093 and program data 1094 stored in the memory 1010 and the hard disk drive 1090 to the RAM 1012 as necessary, and executes various processing procedures.

Note that the program module 1093 and program data 1094 related to the onset probability determination program are not limited to being stored in the hard disk drive 1090, but may be stored in a removable storage medium, for example, and read out by the CPU 1020 via a disk drive or the like. It's okay. Alternatively, the program module 1093 and program data 1094 related to the onset probability determination program are stored in another computer connected via a network (LAN (Local Area Network), WAN (Wide Area Network), etc.), and are stored in another computer connected to the network interface 1070. The data may be read by the CPU 1020 via the CPU 1020.

The above-described embodiments and modifications thereof are included in the technology disclosed in this application, and are also included in the scope of the invention described in the claims and its equivalents.

10 Information processing device 11 Communication processing section 12 Control section 12a Collection section 12b Learning section 12c Acquisition section 12d Judgment section 12e Output section 13 Storage section 13a Meal data storage section 13b User data storage section 13c Model storage section 20A, 20B Terminal device 30 Network

Claims (4)

Meal menus are identified from the vital data of multiple users who have developed brain diseases and images of the meals consumed by each user, and the nutritional values corresponding to the identified meal menus are collected as meal data. In addition, a collection unit that collects disease information on whether or not each user's brain disease has recurred and when it has recurred;
a learning unit that uses the vital data and the dietary data to learn, by supervised learning, a model that determines the probability that a user will develop a brain disease again, using the disease information collected by the collection unit as a correct label;
an acquisition unit that identifies vital data of a specific user and a meal menu ingested by the specific user, and acquires nutritional value corresponding to the specified meal menu as meal data ;
a determination unit that uses the vital data and the dietary data acquired by the acquisition unit as input data and uses a model learned by the learning unit to determine the probability that the specific user will develop a brain disease;
and an output section that outputs a probability that the specific user will develop a brain disease determined by the determination section. The collection unit collects the vital data, the meal data, and the disease information, and also collects any one or more of the plurality of users' brain wave information, blood information, activity information, and speech information. Collect more and more
The learning unit collects any one or more of the vital data, the meal data, the disease information, the brain wave information, the blood information, the activity information, and the voice information collected by the collection unit. The information processing apparatus according to claim 1 , wherein the model is learned using a plurality of models. An onset probability determination method executed by an information processing device, the method comprising:
Meal menus are identified from the vital data of multiple users who have developed brain diseases and images of the meals consumed by each user, and the nutritional values corresponding to the identified meal menus are collected as meal data. In addition, a collection step of collecting disease information as to whether or not each user's brain disease has recurred and when it has recurred;
a learning step of learning, by supervised learning, a model for determining the probability that a user will develop a brain disease again, using the vital data and the dietary data, using the disease information collected in the collecting step as a correct answer label;
an acquisition step of identifying the vital data of a specific user and the meal menu ingested by the specific user, and acquiring the nutritional value corresponding to the specified meal menu as meal data ;
a determination step of determining the probability that the specific user will develop a brain disease using the model learned in the learning step using the vital data and the dietary data acquired in the acquisition step as input data;
an outputting step of outputting the probability that the specific user will develop a brain disease determined in the determining step. Meal menus are identified from the vital data of multiple users who have developed brain diseases and images of the meals consumed by each user, and the nutritional values corresponding to the identified meal menus are collected as meal data. Also, a collection step of collecting as disease information whether or not each user's brain disease has recurred and when it has recurred;
a learning step of learning, by supervised learning, a model for determining the probability that a user will develop a brain disease again, using the vital data and the dietary data, using the disease information collected in the collecting step as a correct answer label;
an acquisition step of identifying vital data of a specific user and a meal menu ingested by the specific user, and acquiring nutritional value corresponding to the specified meal menu as meal data ;
a determination step of determining the probability that the specific user will develop a brain disease using the model learned in the learning step using the vital data and the dietary data acquired in the acquisition step as input data;
and an outputting step of outputting the probability that the specific user will develop a brain disease determined in the determining step.

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