Preventive medical support system and preventive medical support method

Abstract

To provide a preventive medicine support system that can analyze the characteristics of a result of medical examination for a user attribute (group) and use the result in decision making for determining continuation of and selecting a policy for preventive medicine.SOLUTION: A preventive medicine support system PMSS has: a classification unit 211 that classifies medical examination result data into groups based on user attribute information; a machine learning unit 12 that extracts medical examination result items involved in the classification into the groups; a medical examination result extraction unit 13 that extracts a characteristic medical examination result from medical examination results for the groups for the medical examination result items extracted by the machine learning unit 12; an output processing unit 14 that outputs the extracted characteristic medical examination result; and a significant difference verification unit 15 that verifies if there is a significant difference for every group by a statistical method for the extracted characteristic medical examination result.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a preventive medical support system and a preventive medical support method. [Background technology]

[0002] In recent years, it is expected that health checkup results will be effectively used as preventive medicine. The health management support device of Patent Document 1 is disclosed to include a storage medium that stores information representing the health condition of a user including information representing the result of the health checkup and information representing lifestyle habits in association with each of the users, a first selection unit that selects a first user to be supported in health management from the multiple users based on the stored information representing the health condition, a second selection unit that selects a second user having a target health condition for the selected first user from the multiple users based on the stored information representing the health condition and the stored information representing the lifestyle habits, a support information generation unit that reads out information representing the lifestyle habits of the selected second user from the storage medium and generates support information for supporting the health management of the first user based on the read information representing the lifestyle habits, and an information output unit that outputs the generated support information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6957324 Summary of the Invention [Problem to be solved by the invention]

[0004] Patent Document 1 aims to provide health management support and support information to individuals, but the current situation is that preventive medical care and health management measures in companies are uniform for all subjects. This has the problem that sufficient analysis has not been done from the perspective of utilizing health check result data and user attribute information to apply measures according to the attributes and health conditions of the subjects.

[0005] The present invention has been made to solve the above-mentioned problems, and aims to provide a preventive medical care support system and a preventive medical care support method that can analyze the characteristics of health checkup results for user attributes (groups) and utilize the results in decision-making for deciding whether to continue preventive medical care or selecting measures. [Means for solving the problem]

[0006] In order to achieve the above object, the preventive medical support system of the present invention is characterized by having a classification unit that classifies medical checkup result data into groups based on user attribute information, a machine learning unit that extracts medical checkup result items related to the classification of the groups, a medical checkup result extraction unit that extracts characteristic medical checkup results from among the medical checkup results for the groups for the medical checkup result items extracted by the machine learning unit, an output processing unit that outputs the extracted characteristic medical checkup results, and a significant difference verification unit that verifies whether there is a significant difference between the groups for the extracted characteristic medical checkup results using a statistical method. Other aspects of the present invention will be described in the embodiments described later. Effect of the Invention

[0007] According to the present invention, the characteristics of health check results for user attributes (groups) can be analyzed, and the results can be utilized in decision-making regarding the continuation of preventive medical care and the selection of measures. [Brief description of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a preventive medical care support system according to an embodiment. [Diagram 2] FIG. 11 is a diagram showing an example of medical checkup result data. [Diagram 3] FIG. 11 is a diagram illustrating an example of user attribute information. [Figure 4] FIG. 11 is a diagram illustrating an example of a user attribute master. [Diagram 5] 13 is a diagram showing an example of medical checkup result data and user attribute information after anonymization processing by an anonymization unit. FIG. [Figure 6]FIG. 13 is a diagram illustrating an example of anonymization processing by an anonymization unit. [Figure 7] FIG. 13 is a diagram illustrating an example of analysis target data. [Figure 8] 13 is a flowchart showing a preventive medical care support process. [Figure 9A] FIG. 1 is a diagram showing examples of health check result items (explanatory variables). [Figure 9B] FIG. 1 is a diagram showing examples of health check result items (explanatory variables). [Figure 9C] FIG. 1 is a diagram showing examples of health check result items (explanatory variables). [Figure 10] FIG. 13 is a diagram showing an example of the importance of health check result items (explanatory variables) determined by the machine learning unit. [Figure 11] FIG. 13 is a violin diagram showing list information of medical checkup result data by the medical checkup result extraction unit. [Figure 12] FIG. 13 is a violin diagram showing the test values ​​of triglycerides for each cluster. [Figure 13] FIG. 13 is a diagram showing characteristics based on the test reference values ​​of neutral fat for each cluster. [Figure 14] FIG. 11 is a diagram showing an example of an output by an output processing unit. [Figure 15] 13 is a diagram showing an example of a verification result by a significant difference verification unit. FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment for carrying out the present invention will be described in detail with reference to the accompanying drawings. Fig. 1 is a block diagram showing a preventive medical support system PMSS according to an embodiment. The preventive medical support system PMSS (Preventive Medical Support System) aims to analyze the characteristics of medical checkup results for user attributes (groups) and to utilize the results in decision-making for the continuation of preventive medical care and the selection of measures. In particular, the present invention aims to "look at the entire group and find current problems."

[0010] The preventive medical support system PMSS is composed of a preventive medical support device 100 and an establishment server 200. The preventive medical support device 100 and the establishment server 200 are communicably connected via a network NW. The establishments include shops, factories, offices, sales offices, banks, branches, schools, temples, hospitals, inns, smelters, mines, power plants, etc.

[0011] The business server 200 is a server that supports processing on the client side, and performs pre-processing for creating the analysis target data 21. The business server 200 has a processing unit 210 and a storage unit 220. The storage unit 220 stores medical checkup result data 221, which is the medical checkup result of the subject, user attribute information 222 indicating the user attributes of the subject, a user attribute master 223 used to classify the subjects by attributes, and the like.

[0012] Here, user attributes are attributes used for analyzing business establishments. For example, if a business establishment has multiple factories, the attribute is by factory. Specifically, this could be Factory A, Factory B, Factory C, Factory D, Factory E, etc. If the factories are distributed throughout Japan, the attribute is by region. Specifically, this could be Kanto region, Chubu region, Kansai region, Chugoku region, Kyushu region, etc. If there is a wide variety of work content, the attribute may be by job type. Specifically, this could be sales, development, distribution, management, general affairs, etc.

[0013] The processing unit 210 has a classification unit 211 that classifies medical checkup result data into groups based on user attribute information, and an anonymization unit 212 that anonymizes customer medical checkup result data 221 and user attribute information 222. The anonymization by the processing unit 210 enables the preventive medical care support device 100 to perform analysis without handling personal information. The preventive medical care support device 100 provides the establishment server 200 with an anonymization tool. The anonymization unit 212 may be included in the processing of the classification unit 211.

[0014] Anonymization tools use a hash algorithm to anonymize data, making it possible to match data after anonymization. Hashing is the process of using a hash function to find a non-reversible, fixed-length value and replace the data. The same hash value is always obtained from the same data, but even slight differences in the data will generate a completely different hash value that is not similar at all.

[0015] 2 is a diagram showing an example of medical checkup result data 221. The medical checkup result data 221 includes 150 items of medical checkup result data, such as name, sex, date of birth, systolic blood pressure, fasting blood glucose, and triglyceride. The name, sex, and date of birth are key information for the name matching process described below.

[0016] When blood is being pumped from the heart to the aorta, the heart contracts to push the blood out, and the pushed-out blood exerts pressure on the aortic vascular wall. Systolic blood pressure refers to the blood pressure in this state.

[0017] 3 is a diagram showing an example of the user attribute information 222. The user attribute information 222 includes name, sex, date of birth, user attribute code, etc. The name, sex, and date of birth are key information for the name matching work.

[0018] Fig. 4 is a diagram showing an example of the user attribute master 223. The user attribute master 223 includes attribute codes and attribute names. In Fig. 4, the attribute names are X group, Y group, Z group, W group, etc. The attribute names correspond to the above-mentioned A factory, B factory, C factory, D factory, E factory, etc.

[0019] 5 is a diagram showing an example of medical checkup result data 221A and user attribute information 222A after anonymization processing by the anonymization unit 212. In the medical checkup result data 221A and the user attribute information 222A, the name among the key information is hashed, and the date of birth is also date-substituted. The date is uniformly converted to the 1st so that the date of birth cannot be identified. The conversion method is not limited to the 1st, and it may be converted to other symbols (for example, **).

[0020] 6 is a diagram showing an example of the anonymization processing S212 by the anonymization unit 212. The anonymization unit 212 converts the medical checkup result data 221 into medical checkup result data 221A and converts the user attribute information 222 into user attribute information 222A through the anonymization processing S212.

[0021] Returning to FIG. 1, the preventive medical support device 100 will be described. The preventive medical support device 100 includes a processing unit 10, a storage unit 20, an input unit 30, an output unit 40, a communication unit 50, etc. The processing unit 10 includes a data creation unit 11 that creates analysis target data 21 from data sent from the establishment server 200, a machine learning unit 12 that extracts health check result items related to group classification, a health check result extraction unit 13 that extracts characteristic health check results from among the health check results for the groups for the health check result items extracted by the machine learning unit 12, an output processing unit 14 that outputs the extracted characteristic health check results, and a significant difference verification unit 15 that verifies whether there is a significant difference between groups for the extracted characteristic health check results using a statistical method, etc.

[0022] The storage unit 20 stores analysis target data 21 (see FIG. 7), health check result item importance information 22 (see FIG. 10), health check result information for groups 23 (see FIG. 11), output information 24 (see FIG. 14), health check result items 25 (see FIG. 9A to FIG. 9C), ratios for each cluster based on standard values ​​26 (see FIG. 13, for example), number of subjects for each cluster based on standard values ​​27 (see FIG. 13, for example), etc. The ratios for each cluster based on standard values ​​26 and number of subjects for each cluster based on standard values ​​27 will be described later.

[0023] In Fig. 1, the processing unit 10 is a central processing unit (CPU) and executes various programs stored in a RAM, a HDD, etc. The storage unit 20 is a HDD and stores various data for the preventive medical support device 100 to execute processing. The input unit 30 is a device for inputting instructions to a computer, such as a keyboard or a mouse, and inputs instructions such as starting a program. The output unit 40 is a display or the like, and displays the execution status and execution results of processing by the preventive medical support device 100. The communication unit 50 exchanges various data and commands with other devices via a network NW.

[0024] The data creation unit 11 performs name matching processing on the medical examination result data 221A and the user attribute information 222A sent from the establishment server 200 to create the analysis target data 21.

[0025] 7 is a diagram showing an example of the analysis target data 21. The analysis target data 21 includes 150 items of health check result data such as hashed name, sex, date-substituted date of birth, systolic blood pressure, fasting blood glucose, and triglyceride, as well as user attribute code (attribute code), attribute name, and the like.

[0026] 8 is a flowchart showing the preventive medical support process S100. As described above, the data creation unit 11 creates the analysis target data 21 (process S110). The machine learning unit 12 extracts health check result items involved in group classification as machine learning (process S120), and outputs the importance of the health check result items (process S130). The health check result extraction unit 13 extracts health check results for each group for the health check result items extracted by the machine learning unit 12, and outputs a list of health check results for the groups (process S140).

[0027] The health check result extraction unit 13 extracts the characteristics of the cluster corresponding to the group (Process S150). In particular, the health check result extraction unit 13 calculates the proportion 26 of each group according to the standard value of the health check result and the number of subjects 27 of each group according to the standard value for the health check result items extracted by the machine learning unit 12. The output processing unit 14 outputs the characteristics of the cluster that is the extracted characteristic health check result (Process S160). The significant difference verification unit 15 verifies whether there is a significant difference between the two selected clusters using a statistical method, and outputs the significant difference verification result (Process S170).

[0028] Each part will be described in detail. 9A, 9B, and 9C are diagrams showing examples of health check result items (explanatory variables). 99 health check result items 25 are shown in Fig. 9A to Fig. 9C. Among these, it is necessary to narrow down the health check result items that are important for preventive medical support.

[0029] Since it is practically difficult to analyze all of the large number of health check result items, the machine learning unit 12 aims to narrow down the analysis to important items, thereby narrowing down the health check result items to those that are important for determining user attributes.

[0030] One of the feature selection methods or variable selection methods is Boruta. The machine learning unit 12 narrows down important items based on their importance by using the feature selection method (Boruta). At that time, tuning is performed so that the quantile of importance becomes the number of items that can be analyzed practically.

[0031] Fig. 10 is a diagram showing an example of the importance of health check result items (explanatory variables) by the machine learning unit 12. Fig. 10 shows the result of narrowing down by machine learning. Referring to Fig. 10, it can be seen that age at time of health check, BMI_test value, waist circumference_test value, general blood test value, red blood cell count_test value, ···, triglyceride_test value, fasting blood glucose_test value, ···, etc. are important.

[0032] FIG. 11 is a violin diagram showing the list information 23a of the medical checkup result data by the medical checkup result extraction unit 13. In each diagram, the horizontal axis indicates the cluster (CL) corresponding to the group, and the vertical axis indicates each medical checkup result data. The clusters in each diagram are CL1, CL2, CL3, CL4, and CL5 from the left, showing five clusters. The violin diagram is a chart that combines a box-and-whisker plot and a density curve, and in addition to expressing the distribution of numerical data like the box-and-whisker plot, the average value is also visualized. The chart also shows the distribution as a density curve. The average (or median) and the interquartile range are drawn inside the violin diagram. The medical checkup result extraction unit 13 examines the change in the average value of each cluster, the degree of distribution, etc., and extracts important medical checkup result data in detail. 11 shows an example of five clusters, but the number is not limited to this and may be changed as appropriate according to the number of groups classified by the classification unit 211 (FIG. 1).

[0033] FIG. 12 is a violin diagram showing the triglyceride test values ​​for each cluster. The horizontal axis shows the five clusters, CL1, CL2, CL3, CL4, and CL5, and the vertical axis shows the triglyceride test values. Health check result data 23c is obtained by excluding the outlying values ​​in health check result data 23b. Looking at health check result data 23b (1), there is almost no difference in the median values ​​for each cluster, but CL2 and CL3 vary widely. Looking at health check result data 23c (2), there is a tendency for CL4 and CL5 to tend to be concentrated at lower values ​​than the other clusters.

[0034] Fig. 13 is a diagram showing the characteristics of each cluster based on the test standard value of triglyceride. Fig. 13 shows the proportion of each cluster based on the standard value 26 and the number of subjects of each cluster based on the standard value 27. Looking at (3) of the proportion of each cluster based on the standard value 26, the proportion of subjects exceeding the triglyceride standard value (30 to 149 mg / dl: normal) is highest in CL2 and lowest in CL5.

[0035] 14 is a diagram showing an example of output by the output processing unit 14. The display screen of the output unit 40 has a display area 24W of the medical check result data extracted by the medical check result extracting unit 13 in the center, and a task bar at the bottom. The task bar has a name matching icon 24A, a machine learning icon 24B, an entire display icon 24C, a selection display icon 24D, a feature extraction icon 24E, a significant difference verification icon 24F, a back button 24M, a forward button 24N, etc.

[0036] When the name matching icon 24A is clicked, the data creation unit 11 starts processing, executes the name matching process, and creates the analysis target data 21 from the data sent from the business server 200. When the machine learning icon 24B is clicked, the machine learning unit 12 starts processing, and extracts the health check result items involved in the classification of groups (clusters).

[0037] When the full display icon 24C is clicked, the medical checkup result extraction unit 13 starts processing, and among the medical checkup result items extracted by the machine learning unit 12, medical checkup result items with high importance are displayed in full view (see FIG. 11). When the user clicks on a medical checkup result item on the full display list screen and then clicks the selection display icon 24D, the medical checkup results are enlarged and displayed. When multiple medical checkup result items are selected, multiple medical checkup results are displayed.

[0038] When the user clicks on a health check result item on the screen in full display or enlarged display and then clicks the feature extraction icon 24E, the health check result extraction unit 13 extracts characteristic health check results from the health check results for the group and displays them as shown in Figure 14.

[0039] 14, when the user clicks two clusters on the screen and then clicks the significant difference verification icon 24F, the significant difference verification unit 15 uses statistical methods to verify whether there is a significant difference between groups (clusters) for the characteristic medical examination results extracted. In this embodiment, as an example, an F-test and a t-test are shown.

[0040] FIG. 15 is a diagram showing an example of the verification result by the significant difference verification unit 15. The significant difference verification unit 15 performs verification of whether there is a significant difference in the test values ​​of the selected clusters by F-test and t-test. In the example of FIG. 15, (1) F-test: a test of variance using two samples is performed. Here, a case is shown in which CL2 and CL5 are selected based on FIG. 13. Since the P value of the variance result using the two samples of CL2 and CL5 is less than 0.05, the null hypothesis (the variances are equal) is rejected. Therefore, it is determined that "the variances of the triglyceride test values ​​of CL2 and CL5 are not equal."

[0041] Next, the significant difference verification unit 15 performs (2) a t-test assuming that the variances are not equal. Since the P-value is less than 0.05 (significance level), the null hypothesis is rejected. In other words, it is determined that "there is a significant difference in the test values ​​of triglycerides between CL2 and CL5." From the verification result of the significant difference verification unit 15, the user can confirm whether or not there is a significant difference in the test value of the selected cluster.

[0042] The preventive medical support system of the present embodiment described above has the following features. (1) The preventive medical support system PMSS of this embodiment is characterized by having a classification unit 211 that classifies medical checkup result data into groups based on user attribute information, a machine learning unit 12 that extracts medical checkup result items related to group classification, a medical checkup result extraction unit 13 that extracts characteristic medical checkup results from the medical checkup results for the groups for the medical checkup result items extracted by the machine learning unit 12, an output processing unit 14 that outputs the extracted characteristic medical checkup results, and a significant difference verification unit 15 that verifies whether there is a significant difference between groups for the extracted characteristic medical checkup results using a statistical method. This makes it possible to analyze the characteristics of medical checkup results for user attributes (groups) and utilize the results in decision-making for deciding whether to continue preventive medical care or select measures.

[0043] In the present embodiment, the preventive medical support device 100 and the establishment server 200 are described separately, but the present invention is not limited to this.

[0044] (2) The classification unit 211 anonymizes the names included in the medical examination result data and the user attribute information by using a hash algorithm, which allows analysis without handling personal information.

[0045] (3) The machine learning unit 12 uses the user attribute information as a response variable and the medical checkup result items as explanatory variables, and outputs the medical checkup result items in order of importance. This makes it possible to narrow down a large number of medical checkup result items, for example up to 150 items, to important items.

[0046] (4) The health check result extraction unit 13 can calculate the proportion of each group based on the standard value of the health check result and the number of subjects in each group based on the standard value for the health check result items extracted by the machine learning unit 12. This makes it possible to clarify the differences between groups (clusters).

[0047] (5) The output processing unit 14 displays on the display unit a diagram including the distribution and average value of the numerical data, with the horizontal axis being the classified groups and the vertical axis being the medical checkup values ​​of the medical checkup result items. This allows the user to understand the differences between groups (clusters). In this embodiment, the display is described as being on the output unit 40 (display unit) of the preventive medical care support device 100, but this is not limiting. The display may be made viewable from the business server 200 via a Web server.

[0048] (6) The significant difference verification unit 15 verifies whether the variances of the groups (clusters) are equal, and verifies the case where the residuals are not equal, and verifies whether there is a significant difference between the groups. This makes it possible to grasp whether there is a difference between the distributions of the two selected groups.

[0049] (7) User attribute information is information that indicates the physical and social characteristics of a user. In this embodiment, preventive medical care within a company is described by factory, region, job type, etc., but is not limited to this. It can be used for group analysis by hospital, etc.

[0050] (8) A preventive medical support method for a preventive medical support device that analyzes characteristics of medical checkup results, the preventive medical support device classifies medical checkup result data into groups based on user attribute information, extracts medical checkup result items related to group classification, extracts characteristic medical checkup results from among the medical checkup results for the extracted medical checkup result items, outputs the extracted characteristic medical checkup results, and verifies whether there is a significant difference between the extracted characteristic medical checkup results by a statistical method. This allows the characteristics of the medical checkup results for the user attributes (groups) to be analyzed, and the results to be used in decision-making for the continuation of preventive medical care and the selection of measures. [Explanation of symbols]

[0051] 10 Processing section 11 Data Creation Department 12 Machine Learning Department 13. Medical examination result extraction section 14 Output Processing Section 15 Significance Verification Section 20 Memory section 21 Data to be analyzed 22 Health check result item importance information Health checkup results information for 23 groups 24 Output Information 30 Input section 40 Output section (display section) 50 Communications Department 100 Preventive medical support device 200 Business Servers 210 Processing section 211 Classification Department 220 Storage section 221 Health checkup result data 222 User Attribute Information 223 User Attribute Master NW Network PMSS Preventive Medical Support System

Claims

1. A classification unit that classifies the medical examination result data into groups based on user attribute information; A machine learning unit that extracts health check result items related to the classification of the groups; A health check result extraction unit that extracts characteristic health check results from the health check results for the group for the health check result items extracted by the machine learning unit; an output processing unit that outputs the extracted characteristic medical examination results; and a significant difference verification unit that uses a statistical method to verify whether there is a significant difference between the groups for the extracted characteristic medical examination results. A preventive medical support system comprising:

2. The classification unit anonymizes the names included in the medical examination result data and the user attribute information by using a hash algorithm.

2. The preventive medical support system according to claim 1.

3. The machine learning unit uses the user attribute information as a target variable, the medical checkup result items as explanatory variables, and outputs the medical checkup result items in order of importance.

2. The preventive medical support system according to claim 1.

4. The health check result extraction unit calculates the ratio of each group based on a standard value of the health check result and the number of subjects for each group based on the standard value for the health check result items extracted by the machine learning unit.

2. The preventive medical support system according to claim 1.

5. The output processing unit displays on a display unit a diagram including a distribution and an average value of the numerical data, with the horizontal axis representing the classified groups and the vertical axis representing the medical checkup values ​​of the medical checkup result items.

2. The preventive medical support system according to claim 1.

6. The significant difference verification unit performs a test to determine whether the variances of the groups are equal, performs a test assuming that the variances are not equal, and verifies whether there is a significant difference between the groups.

2. The preventive medical support system according to claim 1.

7. The user attribute information is information indicating physical and social characteristics of the user.

6. The preventive medical support system according to claim 1,

8. A preventive medical support method for a preventive medical support device that analyzes characteristics of a medical examination result, comprising: The preventive medical support device includes: Classify the medical checkup result data into groups based on user attribute information, Extracting health check result items related to the classification of the groups; Extracting characteristic health check results from among the health check results for the group for the extracted health check result items; Outputting the extracted characteristic medical examination results; The extracted characteristic health check results are verified using statistical methods to see if there are significant differences between the groups. A preventive medical support method comprising:

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