JP2009273558A - Medical checkup supporting apparatus and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a medical checkup supporting apparatus which makes a highly accurate evaluation of a disease risk by taking a diathesis or a hereditary component into consideration and evaluates an influence an individual and a group to which the individual belongs mutually impose in terms of the disease risk and a program. <P>SOLUTION: The medical checkup supporting apparatus is equipped with: a sorting means 8 which extracts and acquires prescribed examinee data from evaluation conditions out of accumulated examinee data and sorts the acquired examinee data into groups from the examinee data to be evaluated and influence item data; a risk calculating means 10 which determines a group to which the examinee data to be evaluated belongs out of the groups created by the sorting means and calculates a ratio of developing the disease related to the evaluation of the disease risk in a range of the evaluation conditions in the group; and a result display means 12 for displaying the result calculated by the risk calculating means 10. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a health check support apparatus and program for providing information contributing to lifestyle improvement by calculating and presenting disease risk.

  Usually, if you want to know your own health condition, or if you want to know how much you are at risk of developing a certain disease if you continue to live with the lifestyle at that time, you should have a health checkup. It is common.

  In the conventional health examination, the condition of the examinee is examined by clinical examination or inquiry, and the result is compared with the average value of the healthy person to diagnose the health condition of the examinee. A person who has received such a health check can roughly know whether or not there is a possibility of developing a certain disease, based on the result of the health check, and can be used as a guideline for improving lifestyle habits.

  By the way, various technologies for supporting guidance on disease prevention and health maintenance based on the results of health examinations and the like have been disclosed. For example, Patent Document 1 discloses a rule data preparation means for analyzing medical examination information data and preparing rule data obtained by correlation rule mining for a combination of conditions and an incidence rate of a disease, and a medical examination result of a guidance target person Health check result input means, a rule search means for searching for a rule corresponding to the condition of the subject from the rule data, and a guidance content display means for displaying the incidence as a result of the search A guidance support system is disclosed.

  According to the technique disclosed in Patent Document 1, a health guidance support system that presents a quantitative and easy-to-understand risk of a disease at the time of health guidance by searching for a condition corresponding to the subject is provided.

  Further, in Patent Document 2, in a health checkup method for predicting a risk for a disease of a person who has undergone a medical examination, information obtained for the person who has undergone a medical examination may affect the onset of the disease. The possibility that the examinee develops the disease using the step of inputting the examinee information indicating the condition, the examinee information of the inputted examinee, and the risk parameter obtained from the disease incidence of the past examinee. And a step of obtaining a risk value indicating how much is present.

According to the technique disclosed in Patent Document 2, a health diagnosis method capable of improving the accuracy of calculating a risk value for a disease of a medical examinee is provided.
JP-A-2005-49960 JP 2000-262479 A

  By the way, the technique disclosed in Patent Document 1 is not a technique for calculating a disease risk in consideration of genetic factors, individual differences (constitution), and the like. Therefore, it must be said that the disease risk presented by the technique disclosed in Patent Document 1 has insufficient accuracy.

  Further, the technique disclosed in Patent Document 2 is a technique for calculating a disease risk in consideration of individual differences by performing a genetic test. However, a disease whose causative gene has not been clarified, a plurality of causative genes and For diseases that are thought to depend on environmental factors, it is not possible to predict disease risk with high accuracy.

  Furthermore, both of the technology disclosed in Patent Literature 1 and the technology disclosed in Patent Literature 2 include, for example, a disease risk in a group unit such as a family unit or a workplace unit, an effect on a disease risk given to a group by an individual, and It is not possible to calculate the specific impact of the group on the disease risk on individuals.

  The present invention has been made in view of the above circumstances, and can perform a highly accurate disease risk assessment in consideration of constitution and genetic factors, and an individual and a group to which the individual belongs are mutually given. An object of the present invention is to provide a health examination support apparatus and program capable of evaluating the influence on disease risk.

In order to achieve the above object, a health check support device according to the present invention according to claim 1 comprises:
A health check support device that performs disease risk assessment using accumulated patient data,
A data input means for inputting the evaluation subject data which is the evaluation subject's data of the evaluation subject of the disease risk evaluation;
Condition input means for inputting evaluation conditions in the disease risk evaluation,
Influence item data acquisition means for acquiring influence item data indicating an influence item for a target disease of the disease risk assessment;
Related party extraction means for extracting the related patient data, which is the patient data of the related party of the evaluation subject, from the stored patient data;
Based on the evaluation conditions, predetermined examiner data is obtained from the accumulated examinee data, and the obtained examiner data is used as the evaluation subject recipient data, the influence item data, and the related examinee. Classification means for classifying into a plurality of groups based on the data,
Of the groups created by the classification means, determine a group to which the evaluation subject patient data belongs, and calculate a ratio of developing the target disease of the disease risk evaluation within the evaluation condition in the group Risk calculating means to
Presenting means for presenting a calculation result by the risk calculating means;
It is characterized by comprising.

In order to achieve the above object, a program according to the present invention described in claim 8 comprises:
A program that causes a computer to function as a health check support device that performs disease risk assessment using stored patient data,
A data input function for inputting the evaluation subject's data, which is the evaluation subject's evaluation subject data of the disease risk evaluation,
A condition input function for inputting evaluation conditions in the disease risk evaluation;
An influence item data acquisition function for acquiring influence item data indicating an influence item for a target disease of the disease risk evaluation;
A related party extraction function for extracting the related examinee data, which is the examinee data of the related party of the evaluation subject, from the stored examinee data; and
Based on the evaluation conditions, predetermined examiner data is obtained from the accumulated examinee data, and the obtained examiner data is used as the evaluation subject recipient data, the influence item data, and the related examinee. A classification function for classifying into multiple groups based on the data,
Of the groups created by the classification function, the group to which the evaluation subject patient data belongs is determined, and the proportion of the disease risk evaluation target disease within the evaluation condition in the group is calculated. Risk calculation function,
A presenting function for presenting a calculation result by the risk calculating means;
Is realized by a computer.

  According to the present invention, it is possible to perform a highly accurate disease risk assessment in consideration of the constitution and genetic factors, and to evaluate the influence on the disease risk that the individual and the group to which the individual belongs to each other. A diagnosis support apparatus and a program can be provided.

  Hereinafter, a health check support apparatus and program according to an embodiment of the present invention will be described with reference to the drawings.

  FIG. 1 is a diagram illustrating a configuration example of a health check support apparatus according to the present embodiment. As shown in the figure, the health examination support apparatus includes a data preparation unit 31 and a risk evaluation unit 33.

  The data preparation unit 31 includes an evaluation target patient data input unit 1, an evaluation target patient data recording unit 2, a patient relationship input unit 3, a patient relationship recording unit 4, and a disease / effect item input unit 5. , Disease / effect item recording means 6, corresponding examinee data extraction means 7, classification means 8, preparation data holding means 9, and examinee data DB 13. The risk evaluation unit 33 includes a risk calculation unit 10, an evaluation condition input unit 11, and a result display unit 12.

  The examinee data input means 1 is a means for inputting examinee data related to an evaluation subject (hereinafter referred to as evaluation subject recipient data). Here, the examinee data is data including medical examination data, basic data, inquiry data, and diagnostic data. Details of these data will be described later.

  The examinee data recording means 2 is means for recording the evaluation subject examinee data input by the examiner data input means 1.

  The examiner relation input means 3 is a means for inputting data related to a related person such as a blood relation or family relation of the examinee (hereinafter referred to as related person data; details will be described later).

  The examiner relation recording means 4 is means for recording the concerned person data inputted by the examiner relation input means 3.

  The disease / effect item input means 5 is a means for inputting a disease and data related to the disease (hereinafter referred to as disease / effect item data; details will be described later).

  The disease / effect item recording means 6 is means for recording the disease / effect item data input by the disease / effect item input means 5.

  The corresponding examinee data extracting means 7 is a means for extracting the examiner data relating to the concerned party of the examinee to be evaluated from the examiner data DB 13 based on the concerned person data recorded in the examinee relation recording means 4. It is.

  The classification means 8 is recorded in the evaluation subject patient data recorded in the visitor data recording means 2, related person data recorded in the visitor relation recording means 4, and disease / effect item recording means 6. This is means for classifying (grouping) the patient data recorded in the patient data DB 13 based on the disease / effect item data that has been recorded. The processing by this classification means 8 will be described in detail later.

  The preparatory data holding means 9 is a recording means for speeding up the process related to the disease risk evaluation by reducing the number of times that the patient data processing by the classification means 8 is required in the disease risk evaluation process described later. . That is, the preparation data holding means 9 is means for holding the examinee data once processed by the classification means 8 as preparation data in a classified state (in a grouped state).

  The risk calculation means 10 refers to the examinee data classified (grouped) by the classification means 8, and how many examinees (recipient data) belong to the group to which the evaluation subject (evaluation subject recipient data) belongs. ), And how many of them are actually suffering from a disease to be evaluated (hereinafter referred to as a target disease) (disease risk evaluation). This calculation is performed based on an evaluation condition input by an evaluation condition input unit 11 described later.

  Which disease is to be evaluated for risk (determination of target disease), and in which period (for example, how many years) the onset risk is evaluated (determination of a period for risk evaluation) These are means for inputting various evaluation conditions in the disease risk evaluation process. This evaluation condition will be described in detail later.

  The result display means 12 is a means for displaying the calculation result by the risk calculation means 10.

  The patient data DB 13 is a database in which a large number of patient data is stored as data used in the disease risk evaluation process. It is assumed that registration of the patient data in the patient data DB 13 is performed by, for example, a predetermined input unit (not shown). However, registration of the patient data in the patient data DB 13 may be performed by any method.

  Hereinafter, the calculation process of the disease risk evaluation by the health check support apparatus according to the present embodiment will be described.

  Prior to the disease risk evaluation process, the following process (input process of related party data, disease / effect item data) is performed.

<Input of related party data>
The user inputs related person data by the examinee relation input means 3. The related party data is data composed of family data and housemate data.

  As shown in FIG. 3, the family data is data indicating a correspondence relationship between an ID of an evaluation target person (hereinafter referred to as “examinee ID”) and an ID of the father / mother of the evaluation target person. . As shown in FIG. 4, the housemate data is data indicating a correspondence relationship between the examinee ID, the ID of a person living with the person to be evaluated, and the date on which the ID is input.

  Specifically, the above-mentioned family data is the data corresponding to which ID among the patient data stored in the patient data DB 13 for the person concerned, such as father, mother, brother, and child. It is data indicating whether it is data.

  Moreover, the said housemate data is data which shows which data is the data of the person living with the evaluation object person in the patient data stored in the patient data DB 13.

  The family data and the cohabitant data input by the examiner relationship input means 3 are recorded in the examiner relationship recording means 4 in the formats shown in FIGS. In FIG. 3 and FIG. 4, the related party data is configured in a table format, but it is naturally not limited to such a format.

  In the family data shown in FIG. 3, the sibling / child ID is not directly recorded, but by searching for the examinee having the same father / mother ID, the sibling / kid ID of the examinee is obtained. It is possible to extract. That is, the sibling / child ID of the examinee can be obtained by performing a search based on the ID of the examinee's father / mother or the spouse's ID of the examinee.

  However, as a family data, it is of course possible to record IDs of brothers, children, etc. in addition to the father ID and mother ID.

The usefulness of the above-mentioned related party data is as follows.
That is, for example, if both parents or any parent has high blood pressure, the child is more likely to have high blood pressure than if not. Further, even if oneself does not have a smoking habit, for example, if a person living in a familiar living environment such as a living person has a smoking habit, the probability that he / she will have lung cancer is doubled.

  Therefore, when risk assessment for a certain disease is performed, it is not sufficient that the accuracy of the disease risk assessment is sufficient by using only the data of the examinee who performs the risk assessment. That is, it is necessary to improve the accuracy of the disease risk assessment by taking into account genetic factors obtained from data such as blood pressure related to parents and the living environment familiar to the examinee.

  In view of such circumstances, the health diagnosis support apparatus according to the present embodiment performs the disease risk evaluation using the above-described related party data.

  In the present embodiment, for convenience of explanation, a case where a family is used as a group to which the evaluation target person belongs is described as an example. However, other persons who make up the living environment of the evaluation target person (the examinee is affected). Of course, it is also possible to use a workplace or school as the giver).

<Input of disease / effect data>
The user inputs disease / effect item data by the disease / effect item input means 5. As shown in FIG. 5, for example, the disease / effect item data includes a certain disease (disease name), an influence item for the disease (for example, an influence item related to the examinee, an influence item related to the family of the examinee, And the influence items related to the housemate, etc.). The disease / effect item data input by the disease / effect item input means 5 is recorded by the disease / effect item recording means 6.

  In other words, in the present embodiment, when a risk for a certain disease is evaluated, an influence item that causes the disease or an influence item that becomes a precursor is referred to. Specifically, taking cerebral infarction (cerebral thrombosis) as an example as shown in FIG. 5, smoking, obesity, diabetes, hyperlipidemia, hypertension, etc. exist as affected items. Therefore, in this example, the amount of smoking, BMI, blood glucose level, LDL / HDL cholesterol, diastolic / systolic blood pressure, father / mother's diastolic / systolic blood pressure, the smoking amount of the housemate, etc. Input as disease / effect item data.

  In FIG. 5, the disease / effect item data is configured in a tabular format, but is not limited to such a format.

  Hereinafter, the calculation process of the disease risk evaluation by the health examination support apparatus according to the present embodiment will be described with reference to the flowchart shown in FIG.

  First, the user inputs evaluation subject patient data by the patient data input means 1 and inputs a desired “disease risk evaluation condition” by the evaluation condition input means 11 (step S1). Here, the examinee data is data including medical examination data, basic data, inquiry data, and diagnostic data.

  The medical examination data is data obtained by health examinations and various examinations such as the body weight, blood pressure, blood glucose level, GOT, and GPT of the examinee.

  The basic data is data of a patient who does not change over time, such as date of birth and blood type.

  The inquiry data is data indicating, for example, the amount of smoking, sleep time, etc., and lifestyle / behavioral habits obtained by the inquiry.

  The diagnosis data is data relating to the person who has received the diagnosis, such as a medical history or a diagnosis result by a doctor.

  By the way, the evaluation target patient data inputted in step S1, that is, the evaluation subject's medical examination data, basic data, inquiry data, and diagnosis data are shown in FIGS. 6, 7, 8, and 9, for example. It is recorded in the patient data recording means 2 in the form.

  With the exception of basic data whose values do not change, for other patient data, when a certain date is specified, each data is stored so that the latest data can be obtained before the specified date. The date and time information indicating whether the data is current is also recorded.

  By recording in this way, in the example shown in FIG. 6, for example, when “April 10, 2000” and “Destination ID = 0005” are designated, the Patient ID as of April 1, 2000 = 0005 (Diastolic blood pressure = 95 mmHg, systolic blood pressure = 150 mmHg, blood glucose level = 160 mg / dL,...).

  In the example shown in FIG. 6 to FIG. 9, each visitor data is recorded in the examinee data recording means 2 in a tabular form separately, but the assessment subject and the assessment subject recipient data are recorded in association with each other. As long as you can, you can take any form.

  In step S1, it is assumed that the user inputs the examinee data manually by using the examiner data input means 1. However, the evaluation subject examinee data already acquired by the medical examination etc. If it exists in the external device of the support device (the evaluation subject's patient data is recorded in a recording device such as a hard disk, memory, CD, etc.), it is acquired from the external device by an appropriate method and used. Is of course good.

  By the way, the “disease risk evaluation conditions” are various conditions when performing the disease risk evaluation by the risk calculation means 10, and specifically, are the following conditions.

  That is, the disease risk evaluation conditions include, for example, as shown in FIG. 10, the person (evaluation target patient) recorded in the patient data recording means 2, the target disease name of the disease risk evaluation, the reference date (described later) The date and time associated with the patient data used for the processing by the classification means 8), and the period (evaluation period) for confirming the onset of the target disease in the disease risk evaluation.

  In the example shown in FIG. 10, the evaluation condition input means 11 is assumed to be an input screen for inputting in a pull-down menu format and a character input format on a predetermined display. The disease risk evaluation condition input by the evaluation condition input unit 11 is used when a disease risk is evaluated by the risk calculation unit 10 in step S4 described later.

  After the processing in the step S1, the corresponding examinee data extraction means 7 reads the evaluation subject's family and other information from the examinee data DB 13 based on the participant data recorded in the examiner relation recording means 4. The data on the examinees of the persons concerned such as the housemates is extracted (step S2).

  For example, when extracting the examinee data of the father with the examinee ID = 1010, the father's ID (ID = 1001) is obtained by referring to the participant data recorded in the examiner relation recording means 4. Is obtained from the examinee data DB 13 (diastolic blood pressure = 80 mmHg, systolic blood pressure = 120 mmHg, etc.).

  After the processing in step S2, the classification means 8 acquires the patient data used for the disease risk evaluation process from the patient data DB 13 based on the disease risk evaluation conditions (for example, the criteria for evaluation years) If 5 years ago is designated as the day, only the patient data that has not yet developed the target disease for the disease risk assessment at the time of 5 years ago is extracted), and these patient data are stored in the disease Classify into several groups based on the influence item data, the evaluation target patient data, and the patient data acquired in step S2 (step S3). The data generated in step S3 is stored in the preparation data holding unit 9 and is output to the risk calculation unit 10.

  By the processing in step S3, the examinee data having close values (similar values) can be extracted together. Here, the number of the groups may be arbitrary, but as the number of examinees included in each group increases, the result of the disease risk evaluation becomes more statistically valid. For example, in an extreme example, when the number of examinees included in the group is one, the disease risk is 0% or 100% depending on the condition of the one person, resulting in a very rough evaluation result.

  Hereinafter, the process by the classification means 8 in step S3 will be described in detail. First, as the simplest classification processing method, there can be mentioned a method in which a predetermined range or threshold value is set for each item and classification is performed based on these.

  For example, in the case of classifying into four groups using the blood glucose level of the examinee and the systolic blood pressure of the examinee's father, the classification can be made as follows.

・ (Group A) Blood glucose level is 130 mg / dL or higher and father's blood pressure is 140 mmHg or higher ・ (Group B) Blood glucose level is 130 mg / dL or higher and father's blood pressure is lower than 140 mmHg ・ (Group C) Blood glucose level A group with a value of 130 mg / dL or less, a father's blood pressure of 140 mmHg or more (Group D), a blood glucose level of 130 mg / dL or less, and a father's blood pressure of 140 mmHg or less, ie, as shown in FIG. 11A. Thus, classification may be performed by setting an appropriate range or threshold value. However, in this method, it is necessary to set a range and a threshold value for each item.

  Therefore, for example, a clustering method such as k-means or Ward method may be used to classify each cluster (cluster A, cluster B, cluster C, cluster D in the figure) as shown in FIG. 11B. good.

  In the examples shown in FIGS. 11A and 11B, for convenience of explanation, two-dimensional classification is performed using two items, “systolic blood pressure (mmHg) of the examinee's father” and “blood glucose level (mg / dL)”. However, based on the disease / effect item data, all the influence items (for convenience, n items) are actually classified into n dimensions.

  By the way, in the classification process by the classification means 8, particularly when a clustering method is used, it is necessary to repeatedly process a large amount of patient data, so that it is expected that the classification process will take some time.

  Therefore, by recording the result of the classification process in the preparation data holding means 9 as preparation data, it is possible to perform disease risk evaluation from the next time onward at a higher speed. Further, when the above-described clustering method is used for the classification process, values (for example, the center value of each cluster in the k-average method) used for the classification process are also stored in the preparation data holding unit 9. Record.

  In addition, the said preparation data holding means 9 is not an indispensable component requirement of the health-diagnosis assistance apparatus which concerns on this one embodiment. If the preparation data holding means 9 is not provided in the health check support device, the above-described classification process is required every time a disease risk assessment is performed, but only a time loss occurs. It is possible to make an evaluation.

  When the classification process in step S3 is completed, the risk calculation means 10 determines which group the evaluation target patient data belongs to among the groups generated in the classification process in step S3, and in the group to which the risk calculation means 10 belongs. The number of people who developed the disease within the period specified by the disease risk evaluation condition is calculated with reference to the diagnosis data (see FIG. 9), and the disease risk is evaluated (step S4).

  Specifically, for example, when a clustering method (for example, k-average method) is used for the classification process in step S3, in this step S4, the group to which the evaluation target patient data belongs is determined as follows.

(Group determination method 1) The group having the closest evaluation object patient data is detected from the cluster center value and the like recorded in the preparation data holding means 9.

(Group determination method 2) The evaluation subject patient data is further added to the result recorded in the preparation data holding means 9, and clustering is performed again.

  When the above (Group Determination Method 2) is used, clustering is performed again. However, since the clustering is performed from a state in which most of the large amount of data has already been processed, the required time is compared with the normal clustering. Can be greatly shortened.

  And the number (total number) of people who belong to the group to which the evaluation object person (evaluation object person data) belongs (reception person data close to the evaluation object person) and those persons (reception person data) are specified. The number of persons who became ill within a given period (the number of examinee data indicating illness) is calculated and output to the result display means 12 as a disease risk evaluation value.

  When the process in step S4 is completed, the result display means 12 displays the disease risk evaluation result as shown in FIG. 12, for example (step S5). In the example shown in the figure, the results of the disease risk evaluation are shown in the population (total number) of conditions similar to those of the evaluation target person, the number of persons who developed the target disease within a predetermined period, the ratio, and the ratio Is displayed by a face display 12a for intuitively reading the image.

  Here, in the face display 12a, 10% of the risk of developing the target disease is assigned to each of the 10 face displays, and the color of the face display is red as many as the percentage of the onset risk (the same figure). In the figure, the hatched lines are shown for convenience).

  Of course, the display format is not limited to the display format shown in FIG. Of course, information such as the date and time (reference date) of the patient data used in the classification process may be added and displayed.

  By the way, the above-described disease risk evaluation process can be used as follows.

≪Disease risk assessment process with multiple people as one unit≫
For example, the above-described calculation process of the disease risk evaluation may be performed by using a plurality of persons as one unit such as a home unit and a workplace unit (here, a case where a home is one unit will be described as an example). In this case, for example, the following data is used as the disease / effect item data.

・ Family composition ・ Home smoking amount (total smoking amount of the whole family)
-Average value of blood pressure-Standard deviation of sleep time Even in this case, the disease risk evaluation process is the same as the process described above, the classification unit 8 performs the classification process, and the risk calculation unit 10 determines which family to be evaluated in any group. The disease risk assessment is performed by counting the number of units (households) in which at least one person has developed in the group.

  In this case, the result display means 12 displays the result of the disease risk evaluation process, for example, as shown in FIG. In the example shown in the figure, the results of disease risk assessment are the results of households who developed the target disease within a specified period in the population (total number) of households with similar conditions as the target household, and their ratios. A face display 12a for intuitively reading the ratio is displayed.

  Here, in the face display 12a, 10% of the risk of developing the target disease is assigned to each of the 10 face displays, and the color of the face display is red as many as the percentage of the onset risk (the same figure). In FIG. 4, the hatched lines are shown for convenience).

  By using the disease risk assessment process described above as follows, it becomes possible to calculate “the effect of the individual on the disease risk on the group” and “the effect of the group on the disease risk on the individual”. .

≪Disease risk assessment calculation process for investigating the impact of the group on the individual disease risk≫
That is, in order to examine “the effect of the population on the disease risk on the individual”, for example, a population with a low risk of developing the target disease is extracted from the population to which the evaluation target does not actually belong, Assuming that the person to be evaluated belongs, the above-described disease risk evaluation process is performed again.

  By comparing the assessment results of the disease risk assessment process based on this assumption and the actual disease risk assessment results, the impact of the group to which the assessment subject currently belongs on the assessment subject is confirmed. be able to.

≪Disease risk assessment calculation process for investigating the impact of individuals on the group's disease risk≫
In addition, in order to examine “influence of disease risk on individuals by the group”, the value of the evaluation subject data is changed (for example, the actual amount of smoking is 20 / week, and 5 temporarily) The disease risk assessment process is performed again (in the case of a week), and the evaluation result of the disease risk assessment process based on this assumption is compared with the actual disease risk assessment result. You can find out how people affect your entire family. That is, in each of the hypothetical condition and the actual condition, the above-described disease risk evaluation process is performed with a plurality of persons as one unit.

≪Calculation process of disease risk assessment when the examinee changes lifestyle habits≫
Assuming that the examinee has changed his / her habits / behavioral habits, dietary habits, etc., the disease risk evaluation process described above with a value related to the condition (for example, the blood glucose level has dropped from 160 mg / dL to 120 mg / dL) It is possible to know how the onset risk changes by comparing the evaluation result by the disease risk evaluation process based on the conditions of the household and the evaluation result by the actual disease risk evaluation process.

  By the way, the series of disease risk evaluation processes described above by the medical examination support apparatus according to the present embodiment is programmed, or after being programmed, the program is read into a storage medium, Can be easily sold and distributed as independent software products, and the technology according to the present embodiment can be used on other hardware.

  As described above, according to the present embodiment, the disease risk can be evaluated with high accuracy in consideration of the constitution and genetic factors, and the disease risk given to the individual and the group to which the individual belongs is mutually. It is possible to provide a medical examination support apparatus and program capable of evaluating the influence.

  Specifically, by using the data of family members (parents, siblings, children, etc.) as genetic elements and constitutions, the risk of disease with higher accuracy is considered in consideration of the susceptibility of individuals to specific diseases. Evaluation results can be calculated and presented.

  In other words, the health diagnosis support apparatus and program according to the above-described embodiment makes it possible to present a disease risk evaluation with high accuracy to an individual and to present an improvement method according to the disease risk evaluation.

  Furthermore, since it is possible to perform disease risk assessment using a group of a plurality of humans such as households as one unit, the factors for maintaining the health of the members of the group and reducing the risk of onset become clear.

  A simulation can be performed by performing the above-described disease risk evaluation calculation process using assumed values / conditions for various parameters. With this simulation, it is possible to grasp the detailed influence of various factors.

  As mentioned above, although this invention was demonstrated based on one Embodiment, this invention is not limited to embodiment mentioned above, Of course, a various deformation | transformation and application are possible within the range of the summary of this invention. is there.

  Further, the above-described embodiments include inventions at various stages, and various inventions can be extracted by appropriately combining a plurality of disclosed constituent elements. For example, even if some constituent requirements are deleted from all the constituent requirements shown in the embodiment, the problem described in the column of the problem to be solved by the invention can be solved, and the effect described in the column of the effect of the invention Can be extracted as an invention.

The figure which shows an example of the system configuration | structure of the health-diagnosis assistance apparatus which concerns on one Embodiment of this invention. The figure which shows the flowchart of the calculation process of the disease risk evaluation by the medical examination assistance apparatus which concerns on one Embodiment of this invention. The figure which shows an example of ancestry data. The figure which shows an example of the housemate data. The figure which shows an example of disease and influence item data. The figure which shows an example of medical examination data. The figure which shows an example of basic data. The figure which shows an example of inquiry data. The figure which shows an example of diagnostic data. The figure which shows an example of the disease risk evaluation conditions input by the evaluation condition input means. The figure which shows an example of the grouping by a classification | category means. The figure which shows an example of the grouping by a classification | category means. The figure which shows one display example of the result of the disease risk evaluation by a result display means. The figure which shows one example of a display of the result of the disease risk evaluation by a result display means in the case of making several people into one unit.

Explanation of symbols

    DESCRIPTION OF SYMBOLS 1 ... Patient data input means, 2 ... Patient data recording means, 3 ... Patient relationship input means, 4 ... Patient relationship recording means, 5 ... Disease / effect item input means, 6 ... Disease / effect item recording means, 7 ... Corresponding examinee data extraction means, 8 ... Classification means, 9 ... Preparation data holding means, 10 ... Risk calculation means, 11 ... Evaluation condition input means, 12 ... Result display means, 12a ... Face display, 13 ... Patient data DB.

Claims (9)

A health check support device that performs disease risk assessment using accumulated patient data,
A data input means for inputting the evaluation subject data which is the evaluation subject's data of the evaluation subject of the disease risk evaluation;
Condition input means for inputting evaluation conditions in the disease risk evaluation,
Influence item data acquisition means for acquiring influence item data indicating an influence item for a target disease of the disease risk assessment;
Related party extraction means for extracting the related patient data, which is the patient data of the related party of the evaluation subject, from the stored patient data;
Based on the evaluation conditions, predetermined examiner data is obtained from the accumulated examinee data, and the obtained examiner data is used as the evaluation subject recipient data, the influence item data, and the related examinee. Classification means for classifying into a plurality of groups based on the data,
Of the groups created by the classification means, determine a group to which the evaluation subject patient data belongs, and calculate a ratio of developing the target disease of the disease risk evaluation within the evaluation condition in the group Risk calculating means to
Presenting means for presenting a calculation result by the risk calculating means;
A medical examination support apparatus comprising:   The health examination support device performs the disease risk assessment using group examinee data which is one recipient data created from a plurality of recipient data constituting a predetermined group as the examiner data. The health check support apparatus according to claim 1.   The health diagnosis support apparatus according to claim 1 or 2, wherein the disease risk evaluation is performed as a simulation when hypothetical data is input as the evaluation target examinee data by the data input means. The patient data is composed of data indicating the date of birth and blood type, data indicating measurement values in various tests, data indicating life / behavioral habits, and data indicating a past disease / diagnosis result,
The health check support apparatus according to claim 1, wherein each of the data is recorded in association with a date on which the data is obtained.   The related examinee data includes the relatives of the evaluation target person, the person related to the workplace to which the evaluation target person belongs, the person related to the religious organization to which the evaluation target person belongs, and the person related to the area where the evaluation target person resides The medical examination support apparatus according to claim 1, wherein the medical examination support data is at least one medical examination data among medical examination data of the person living with the evaluation target.   The health check support apparatus according to claim 1, further comprising preparation data recording means for recording the group created by the classification means.   The presenting means displays the value of the ratio calculated by the risk calculating means and a graphic representing the ratio symbolically. The medical examination support apparatus according to one. A program that causes a computer to function as a health check support device that performs disease risk assessment using stored patient data,
A data input function for inputting the evaluation subject's data, which is the evaluation subject's evaluation subject data of the disease risk evaluation,
A condition input function for inputting evaluation conditions in the disease risk evaluation;
An influence item data acquisition function for acquiring influence item data indicating an influence item for a target disease of the disease risk evaluation;
A related party extraction function for extracting the related examinee data, which is the examinee data of the related party of the evaluation subject, from the stored examinee data; and
Based on the evaluation conditions, predetermined examiner data is obtained from the accumulated examinee data, and the obtained examiner data is used as the evaluation subject recipient data, the influence item data, and the related examinee. A classification function for classifying into multiple groups based on the data,
Of the groups created by the classification function, the group to which the evaluation subject patient data belongs is determined, and the proportion of the disease risk evaluation target disease within the evaluation condition in the group is calculated. Risk calculation function,
A presenting function for presenting a calculation result by the risk calculating means;
A program characterized by causing a computer to realize the above.   The program performs the disease risk evaluation using group examinee data which is one examinee data created from a plurality of examinee data constituting a predetermined group as the examiner data. The program according to claim 8.

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