JP2006343792A - Health guidance support system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a health guidance support system which presents health improvement support information being helpful for prophylaxis, objective and concrete for each client in health guidance at a medical checkup. <P>SOLUTION: This system has a medical checkup data storage means which stores a plurality of medical checkup data of a plurality of clients, a rule making means which makes a rule for correlating a first group which satisfies a first condition at a first point of time, a second group which satisfies the first condition and a second condition at a second point of time with a predetermined interval from the first point of time, a first number of persons which satisfies a specific state in the first group, a second number of persons which satisfies a specific state in the second group, percentages of the first and second numbers of persons in the first and second groups, first and second percentages for each combination of the first and second conditions for each medical checkup data at the first point of time and the second point of time, a means for storing the created rule, a means for inputting a present value and a target value of the medical checkup data of the client, a means for extracting a rule with which the first condition satisfies the present value and the second condition satisfies the target value, and first and second percentage display means corresponding to the extracted rule. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  It relates to a health guidance support system that supports health guidance for health check-up recipients from the results of health examinations.

Currently, lifestyle-related diseases such as diabetes, hypertension, and hyperlipidemia are a problem. For the purpose of screening and prevention of diseases typified by lifestyle-related diseases, regular health examinations (hereinafter referred to as health examinations) are conducted in the workplace and community.
Lifestyle-related diseases are said to have a large effect due to the accumulation of dietary content, exercise amount, etc. over many years, and often develop with almost no subjective symptoms. It is also possible to prevent it beforehand. For this reason, at health check-up facilities, it is becoming increasingly important to improve services for preventing disease and promoting health, such as providing health guidance based on the results of health check-ups.
For example, in Japanese Patent Publication No. 2582203, an examination result and an inquiry item are indexed on the basis of a table prepared in advance based on an average value for each item and the like as an input. The technology to create health guidance content is introduced.

Japanese Patent Publication No. 2582203

In this health guidance, general and uniform guidance on lifestyle such as “Let's lose weight”, “Let's refrain from smoking and drinking”, “Let's increase exercise” is often performed. . However, there are cases where the guidance content is the general content as described above, or the grounds are not shown, and there is a possibility that it does not necessarily lead to improvement of lifestyle habits. In addition, quantitative analysis using actual data makes it difficult to collect and follow up a large amount of health checkup results (hereinafter referred to as health checkup information), and how much weight should be reduced and how much exercise can be increased. Specific guidance, such as whether it should be, was difficult.
The purpose of the present invention is to detect changes in test values and lifestyle habits of the examinee and subsequent lifestyle-related diseases based on information such as test values and doctor's diagnosis results included in the accumulated past medical examination information. The purpose is to provide a health guidance support system that presents objective and specific health improvement support information for each individual who is useful for disease prevention by analyzing the relationship with the onset of the main diseases.

  The above problem is that medical examination data including disease information related to a change in health condition or the onset of disease, one or more types of medical examination results including examination results and lifestyle habit information is obtained from a plurality of examination times for a plurality of examinees. A combination of one or more types of health check results at the first time point with respect to the health check data at the first time point and the second time point with a predetermined interval A first condition, a combination of one or more types of the medical examination results at the second time point is a second condition, the examinees that satisfy the first condition are the first group, the first condition is satisfied, and The number of the patients who satisfy the second condition is the second group, the number of the patients whose disease information satisfies a specific state or value in the first group is the first number, and the second group The disease information in the specific condition or The number of the examinees satisfying the value is the second number, the ratio of the first number of persons to the number of the first group is the first ratio, and the number of the second number of persons to the number of the second group A rule storage means for storing a rule associating the first ratio and the second ratio for each combination of the first condition and the second condition, with the ratio being a second ratio; Input means for receiving inputs of a current value indicating a health check result and a target value indicating the health check result as a future target of the user; the first condition satisfies the current value; and the second condition is Health guidance comprising: extraction means for extracting the rule that satisfies the target value; and display means for displaying the first ratio and the second ratio corresponding to the extracted rule Based on past performance by support system In addition, it is easy to understand to the user how much a person who has the same health condition as the user's current health condition has developed lifestyle-related diseases, and how much the incidence will change when lifestyle changes are subsequently changed. However, I was able to solve it.

  Further, the above-described problem is that the extraction means extracts the rule that the first condition satisfies the current value and the second ratio satisfies a predetermined value as a recommended rule, and the display means The health guidance support system characterized by displaying the second condition corresponding to the recommended rule as a recommended target value, and how to change lifestyle to reduce the current incidence rate in the future , It was possible to solve it while automatically presenting it to the user.

  Further, the above problem is that the input means accepts input of a plurality of target values with respect to the target value, the extraction means extracts the rule for each of the plurality of target values, and the display means is extracted. The health guidance support system is characterized in that the second ratio corresponding to the rule is displayed in a format that can be compared for each of the plurality of target values. In addition, it was made possible to solve the problem by showing to the user in an easy-to-understand manner how much the incidence changes when each goal is achieved, and how much the incidence varies depending on the target.

Further, the above-described problem is achieved by a health guidance support system, wherein the display means displays the ratio of the second ratio corresponding to the extracted rule as a relative risk between the plurality of target values. , "How much relative risk differs depending on how you change your lifestyle," The problem was solved while presenting it to the user in an easy-to-understand manner.
Further, the above problem is that the display means, the rule storage means, for each rule, the first number of persons, the second number of persons, the number of persons in the first group, the number of persons in the second group. The information is stored in association with each other, and the display means displays the first number of persons, the second number of persons, the number of persons in the first group, and the number of persons in the second group corresponding to the rule. Based on what kind of data, how about the incidence rate of examinees who have the same health condition as the user's current health status and the incidence rate of those who have changed their lifestyle later It was made possible to solve the problem while presenting it to the user in an easy-to-understand manner.

  In addition, the above problem can be created by a health guidance support system characterized by having a rule creation means for creating the rule based on the medical examination data. While trying to solve it.

  According to the present invention, objective information is shown as to how much the incidence changes when test values change or lifestyle changes among past examinees showing the same health condition as the user. Therefore, it is possible to support the health guidance of the instructor and to set goals for improving the lifestyle habits of the examinee. In addition, by providing concrete results using actual data, it is possible to give a strong awareness of the examinee about improvement of lifestyle habits for disease prevention. In addition, by using health examination information to extract specific factors that may lead to lifestyle-related disease signs and disease prevention by individual, more effective measures such as providing individual health management programs based on actual data. Health promotion support system that supports general disease prevention and health promotion can be realized.

  FIG. 1 is a diagram illustrating a configuration example of a health improvement system 100 according to an embodiment of the present invention. The system 100 includes a control unit 101, a medical examination data storage device 102, a rule storage device 103, a rule creation unit 104, an input unit 105, an extraction unit 106, and a display unit 107. . Although the system 100 is described as a hardware configuration, the functions of the system 100 may be configured by software. The system 100 is connected to an input / output terminal 110. The input / output terminal 110 is assumed to be an information device such as a personal computer having a keyboard and mouse as input functions and a CRT display as an output function, but may have other input / output functions. Further, although the present system 100 is described as hardware different from the input / output terminal 110, the input / output function of the input / output terminal 110 may be mounted on the system 100.

  FIG. 2 is a diagram illustrating an example of the medical examination data storage device 102. The medical examination data storage device 102 stores a medical examination ID field 201 for storing a medical examination ID for identifying medical examination data, a medical examiner ID field 202 for storing a medical examiner ID for identifying a medical examinee, and a medical examination year. Consultation year field 203, basic information field 204 for storing various basic information of the examinee, test value information field 205 for storing various test value information of the examinee, and various lifestyle information of the examinee A lifestyle information field 206 to be stored, and a disease information field 207 to store disease information indicating whether the examinee has developed a specific disease. In the example of FIG. 2, the gender and age are the basic information of the examinee, and BMI (Body Mass Index), blood glucose, TC (Total Cholesterol), etc. The number of drinks per week, the number of smokers, the presence or absence of snacks, exercise time (walking), etc. are stored as lifestyle information, and information indicating that diabetes has developed is stored as disease information. The medical examination data storage device 102 may include the marriage status, past history, current medical history, family history, etc. as basic information of the examinee. The medical examination data storage device 102 may include test values such as urine sugar, HbA1c, neutral fat, HDL-cholesterol, and LDL-cholesterol as the test value information of the examinee. The medical examination data storage device 102 may include sleeping hours, eating habits, exercise habits, and the like as the lifestyle habit information of the examinee. In addition, the medical examination data storage device 102 has developed other lifestyle-related diseases (hyperlipidemia, hypertension, obesity, hyperuricemia, etc.) or diseases other than lifestyle-related diseases as the disease information of the examinee. May be included.

The medical examination data storage device 102 can extract annual changes in test values and lifestyle habits of the same examinee, when a specific disease has occurred, and the like. For example, in FIG. 2, the test values of the examinee with the examinee ID = P001 are BMI 27 → 26 → 24.5, blood glucose 115 → 110 → 95, TC 200 → 195 → from 1999 to 2001. It can be seen that the values change to 185 respectively. In addition, the lifestyle of the examinee with the examinee ID = P001 was “drinking 5 times a week” and “between snacks” in 2000, but “drinking 2 times a week” in 2001, It can be seen that each has changed to “do not eat between meals”. In addition, it can be seen that the examinee with the examinee ID = P002 did not develop diabetes in 2000, but developed diabetes in 2001.
In this embodiment, first, based on the medical examination data, a rule for the incidence of whether or not a specific disease has developed and a change in the health condition is created. Next, when the user inputs a current health condition and a future health condition (target), a rule that matches the input content is extracted, and the current incidence rate for a specific disease and the incidence rate after the goal is achieved are displayed.

FIG. 3 is a flowchart showing the operation of the system 100.
When this system starts processing, first, the control unit 101 activates the rule creation unit 104 to create a rule for the occurrence of a specific disease based on the medical examination data stored in the medical examination data storage device 102. Step 301 stored in the rule storage device 103 is executed.
FIG. 4 is a diagram illustrating an example of the rule storage device 103 created in step 301 regarding the onset of diabetes. The rule storage device 103 stores a rule ID field 401 for storing a rule ID for identifying a rule, a first condition field 402 for storing a first condition constituting the rule, and a second condition for storing a second condition constituting the rule. Condition field 403, corresponding number of patients field 404 for storing the number of patients corresponding to the data satisfying the first condition and the second condition, and satisfying the first condition and the second condition, and diabetes during the consultation period The onset visitor number field 405 for storing the number of examinees who have developed an onset, and the onset rate field 406 for storing the onset rate which is a ratio of 405 to 404. The first condition is composed of a combination of medical examination data of each examinee in the first consultation year, and the second condition is composed of a combination of medical examination data of the next year of each examinee. In the example of FIG. 4, as a rule of rule ID = R000, a test subject “male, 40s, BMI 25 or higher, blood sugar level of less than 126 mg / dl, snack” has developed 264 diabetes mellitus in the past 1100 people The rate is 24.0%. In addition, as for the rule of rule ID = R002, 48 out of 260 past examinees who are “male in their first visit, 40s, BMI 25 or higher, blood glucose level of less than 126 mg / dl, satisfied with snacking, less than BMI 25 of next year” Human diabetes develops, and the incidence is 18.5%. In step 301, the above rules are comprehensively created and stored in the rule storage device 103 for all combinations of gender, age, test values, and lifestyle habits included in the medical examination data.
Next, the control unit 101 activates the input unit 105, and executes step 302 that accepts input of the user's basic information, test values, current values of lifestyle information and target values as rule extraction conditions.

  FIG. 5 is a diagram showing an example screen 500 of the input / output terminal 110 in step 302. The screen example 500 includes a basic information input area 510, a test value and lifestyle information input area 520, a result output area 530, an execution button 540, and an end button 541. The basic information input area 510 includes a field for inputting basic information. In the screen example 500, a column 511 for inputting the gender of the examinee and a column 512 for inputting the age of the examinee are displayed. The inspection value and lifestyle information input area 520 includes a field for inputting the user's current value and future target value for the inspection value and lifestyle information. In the screen example 500, fields 521 to 526 for inputting the current value and target value of the examinee are displayed for the BMI, blood sugar level, and snacking habits. The result output area 530 includes a column in which the incidence is displayed. In the screen example 500, a column 531 for outputting the current onset rate and a column 532 for outputting the onset rate after achieving the target are displayed.

  FIG. 6 is a diagram showing a screen example 600 of the input / output terminal 110 when the user inputs a value in each column after step 302 is executed. In the screen example 600, gender male (611), age 40s (612) as basic information, current values of test values and lifestyle information BMI 25 or more (621), blood glucose level less than 126 mg / dl (623), snack (625), the target values of the test value and the lifestyle information are entered as BMI less than 25 (622), blood glucose level less than 126 mg / dl (624), and snack between (626).

  Next, when the user presses the execution button 640 in the screen example 600, the control unit 101 activates the extraction unit 106, and (1) the basic information input from the rule storage device 103 is configured only by (1) the first condition. Information, test values, and current values of lifestyle information satisfy the first condition, and (2) basic information, test values, and lifestyle information entered by the user, consisting of the first condition and the second condition. Step 303 is executed to extract a rule that satisfies all the first conditions of the current value and satisfies the second condition of the inspection values and lifestyle information target values input by the user.

When step 303 is executed under the input example of the screen example 600, in the above (1), the rule with the rule ID = R000 is extracted from 611, 612, 621, 623, 625 from FIG. In (2) above, a rule with rule ID = R050 is extracted from 611, 612, 621, 622, 623, 624, 625, 626 from FIG.
Next, the control unit 101 activates the display unit 107 and executes step 304 that outputs the current onset rate and the onset rate after achieving the target based on the rule extracted in step 303.
FIG. 7 is a diagram showing a screen example 700 when step 304 is executed under the input example of the screen example 600. In the screen example 700, the onset rate “24.0%” of the rule (1) (rule ID = R000) extracted in step 303 is output to the column 731 for outputting the current onset rate. The onset rate “16.7%” of the rule (2) (rule ID = R050) extracted in step 303 is output to the column 732 for outputting the onset rate. As a result, based on the results of past examinees, how many of the examinees showing the same health condition as the current user have developed for a specific disease, and the test values and lifestyle habits are the target values. It is possible to provide the user with an easy-to-understand description of how the incidence changes when changing to.

  Next, the control unit 101 executes an end determination step 305. When the user presses an end button 741 in the screen example 700, the system ends. If the user does not press the end button 741, the process returns to step 302 so that a new condition input can be accepted.

  In step 302, the user inputs only basic information, current values of test values and lifestyle information, and the extraction unit 106 extracts a rule satisfying a predetermined onset rate as a recommended rule, and displays the display unit. 107 may output the second condition constituting the recommended rule as a target value. For example, when a rule with the lowest incidence after the achievement of the target is extracted as a recommended rule, when the user inputs only 611, 612, 621, 623, 625 and presses the execution button 640 in the screen example 600, FIG. 4, the rule ID = R051 with the lowest onset rate is extracted as a recommended rule from the rules (rule ID = R000 to R051) satisfying 611, 612, 621, 623, 624 as the first condition. As a value, “BMI less than 25, blood glucose level less than 126, not snacking”, an onset rate “9.7%” is displayed. This makes it possible to automatically set goals in response to various needs, such as how to change test values and lifestyle habits in order to reduce the incidence most. It can be improved.

  In step 302, the user inputs a plurality of test values and target values of lifestyle information, the extraction unit 106 extracts a plurality of corresponding rules using each of the plurality of targets as a second condition, and the display unit 107 , It may be output in a format in which the incidence rates of the plurality of extracted rules can be compared. Moreover, you may display the ratio of the incidence of several extracted rules as a relative risk of several targets. FIG. 8 is a diagram showing a screen example 800 after execution of step 304 when a plurality of test values and target values of lifestyle habit information are input. In the screen example 800, target 1 (801 to 803) and target 2 (805 to 807) are a plurality of target values input by the user, and rules (rule ID = R050, R051) extracted as the second condition. ), The onset rate “16.7%” (804) after achieving the target 1 and the onset rate “9.7%” (808) after achieving the target 2 are displayed. Further, the ratio of 808 to 804, 9.7 / 16.7 = 0.58, is displayed as the relative risk “0.58 times” (809) for achieving goal 2 with respect to achieving goal 1. That is, it can be seen that by changing the target 1 “to snack” (803) to the goal 2 “do not snack” (807), the onset rate is relatively reduced to 0.58 times. This makes it easy for the user to understand how much the incidence changes for each goal and how much the incidence and relative risk differ depending on how lifestyle changes. Since it can be provided, the convenience of the user can be improved.

  In the present embodiment, the onset rate is displayed, but the number of examinees corresponding to the extracted rules and the number of examinees who developed among them may be displayed. As a result, it is possible to provide information on the basis of what kind of data the displayed incidence is extracted, and it is possible to improve user convenience.

In the present embodiment, the first checkup year is used as the first condition of the rule, and the next year checkup data is used as the second condition. However, checkup data in other checkup years may be used. As a result, users can respond to various needs with respect to the onset rate to be displayed, such as the onset rate when the goal is achieved by one year and the onset rate when the goal is achieved by two years. Can improve convenience.
In this embodiment, as a condition constituting the rule, a section is set for items that can take continuous values such as age 40s, BMI 25 or more, and blood glucose level less than 126. However, the age is 40 years old and BMI 25.5 is set. The condition may be set at an arbitrary value such as a blood glucose level of 130 mg / dl. For items that take non-sequential values, such as “smoking, not smoking, quitting smoking”, it may be possible to set conditions that combine arbitrary values such as “do not smoke or quit”. Thereby, since the rule corresponding to various needs regarding a target can be created, the convenience of a user can be improved.

  In the present embodiment, as an example of the rule created in step 301, a rule related to the onset of diabetes was shown. However, a rule related to the onset of a disease other than diabetes is created using the disease information included in the medical examination data storage device 102. May be. In addition to sex, age, BMI, blood glucose level, and snacking habits, the rule may include all items stored in the medical examination data storage device 102. Moreover, you may change the item contained in a rule according to a disease. For example, in the case of high blood pressure, blood pressure, the intake of a salty meal, etc. can be considered. In addition, in the case of hyperlipidemia, TC, the intake of a meal with a lot of oil, etc. can be considered. As a result, it is possible to meet various needs related to the target disease and the items constituting the rule, so that convenience for the user can be improved.

The combination of the items of the medical examination data constituting the rule may be intentionally set by the user when creating the rule. For example, the gender and age are not intentionally changed by the user setting a target. Therefore, in the example of the rule storage device 103 in FIG. 4, gender and age are not included in the second condition. In addition, for example, smoking has generally been pointed out that the risk of various diseases may increase, and it is better not to smoke than to smoke, and the same guidance is often given in health guidance. Depending on the health check-up data, the rule of continuing to smoke may be lower than the rule of quitting smoking. Such a rule that is determined not to be useful or useful for health guidance may be intentionally deleted (not stored in the rule storage device 103) at the time of rule creation. In this way, a highly practical system can be constructed by optimizing the rules to be created, and the time required for rule extraction can be reduced by reducing the number of rules accompanying the optimization.
In this embodiment, a process for creating a rule from medical examination data (step 301) and a process for inputting a condition and extracting a rule and displaying an onset rate (step 302 to step 304) are configured as one system. Although the case has been described, these processes may be performed by another system. For example, doctors and public health nurses create rules in advance and update the rule database on the Web server, so that the examinee can input conditions from a home or company terminal via a Web browser and refer to the incidence rate An operation form using a Web service is conceivable. As a result, it is possible to construct a system corresponding to various operation forms for each medical examination facility.

  As described above, this system objectively shows how much the incidence changes when test values change or lifestyle changes among past examinees showing the same health condition as the user. Because it can be presented as realistic information, it is possible to support the health guidance of the instructor and to set goals for improving the lifestyle habits of the examinee.

  In addition, by providing concrete results using actual data, it is possible to give a strong awareness of the examinee about improvement of lifestyle habits for disease prevention.

  In addition, by using health examination information to extract specific factors that may lead to lifestyle-related disease signs and disease prevention by individual, more effective measures such as providing individual health management programs based on actual data. Health promotion support system that supports general disease prevention and health promotion can be realized.

The figure which shows the structural example of the health improvement system which is an Example of this invention. The figure which shows the example of a medical examination data storage device. The flowchart which shows operation | movement of this system. The figure which shows the example of the rule memory | storage device produced regarding the onset of diabetes. The figure which shows the example of a screen of the input / output terminal in step 302. FIG. The figure which shows the example of a screen of an input / output terminal when a user inputs a value into each column after execution of step 302. The screen example of the input-output terminal after step 304 execution. The figure which shows the example of a screen after step 304 execution at the time of inputting the test value and the target value of lifestyle information a plurality.

Explanation of symbols

100 system, 101 control unit, 102 medical examination data storage device, 103 rule storage device, 104 rule creation unit, 105 input unit, 106 extraction unit, 107 display unit, 110 input / output terminal,
201 medical examination ID field, 202 examinee ID field, 203 medical examination year field, 204 basic information field, 205 test value information field, 206 lifestyle information field, 207 disease information field,
301 rule creation step, 302 condition input step, 303 rule extraction step, 304 result display step, 305 end determination step,
401 Rule ID field, 402 First condition field, 403 Second condition field, 404 Applicable patient number field, 405 Onset patient number field, 406 Incidence rate field,
500 screen examples, 510 basic information input area, 511 gender input field, 512 age input field, 520 test value and lifestyle information input area, 521 current BMI input field, 522 target BMI input field, 523 current blood glucose level input Field, 524 Target blood sugar level input field, 525 Current snack food habit input field, 526 Target snack food habit input field, 530 Result output area, 531 Current onset rate output field, 532 Onset rate output field after goal achievement, 540 Execute button, 541 end button,
600 screen example, 611 gender input field, 612 age input field, 621 current BMI input field, 622 target BMI input field, 623 current blood sugar level input field, 624 target blood sugar level input field, 625 current snacking habits input Field, 626 target snacks input field, 640 execution button,
700 Screen Example, 731 Current Incidence Rate Output Column, 732 Incidence Rate Output Column after Achievement of Target, 741 Exit Button,
800 screen example, 801 Target 1 BMI input field, 802 Target 1 blood glucose level input field, 803 Target 1 snack food input field, 804 Onset rate output field after target 1 achievement, 805 Target 2 BMI input field, 806 Target 2 blood sugar level input field, 807 Target 2 snacking habit input field, 808 Onset rate output field after target 2 achievement, 809 Relative risk output field of target 2 achievement relative to goal 1 achievement.

Claims (5)

Health that stores medical examination data that includes one or more types of medical examination results including disease information related to changes in health status or the onset of disease, test results, and lifestyle habits information across multiple examinees. Diagnostic data storage means;
For the medical examination data at the first time point and the second time point at a predetermined interval from the first time point, a combination of one or more types of the medical examination results at the first time point is a first condition. , A combination of one or more types of the medical examination results at the second time point is a second condition, the examinees who satisfy the first condition are the first group, the first condition is satisfied, and the second condition is The number of the examinees satisfying the condition is the second group, the number of the examinees whose disease information satisfies the specific state or value in the first group is the first number, and the disease information is the second group. The number of the examinees satisfying the specific condition or value is the second number, the ratio of the first number to the number of the first group is the first ratio, and the number of the second group is The ratio of the second number of people as the second ratio, and the first condition and the previous And rules creation means for creating a rule that associates the combination the second ratio of the first rate for each of the second condition,
Rule storage means for storing the created rule;
Input means for receiving at least the current value, with the checkup data of the checkout as the current value and the checkup data as the checkpoint of the checkout as the target value;
Extraction means for extracting the rule that satisfies the current value that is input with the first condition and that satisfies the target value that is input with the second condition;
A health guidance support system comprising display means for displaying the first ratio and the second ratio corresponding to the extracted rule.   The health guidance support system according to claim 1, wherein the extraction unit is configured to recommend the rule that the first condition satisfies the current value and the second ratio satisfies a predetermined value. And the display means displays the second condition corresponding to the recommended rule as a recommended target value.   The health guidance support system according to claim 1, wherein the input unit accepts input of a plurality of target values with respect to the target value, and the extraction unit extracts the rule for each of the plurality of target values, The health guidance support system, wherein the display means displays the second ratio corresponding to the extracted rule in a format that can be compared for each of the plurality of target values.   The health guidance support system according to claim 3, wherein the display means displays the ratio of the second ratio corresponding to the extracted rule as a relative risk between the plurality of target values. A characteristic health guidance support system. 4. The health guidance support system according to claim 1, wherein the rule storage unit includes, for each rule, the first number of persons, the second number of persons, the number of persons in the first group, The number of people in the second group is stored in association with each other, and the display means has the first number of people, the second number of people, the number of people in the first group, and the number of people in the second group corresponding to the rule. A health guidance support system characterized by displaying,.

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