JP2012014582A - Transition destination level prediction method, apparatus, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To highly accurately predict how present examination values may change in future by considering physiological characteristics from both group and individual aspects.SOLUTION: The examination value distribution of examination items in predetermined record groups within a health check database having data amount statistically processible for each of medical examination items is divided into a predetermined number of levels in such a manner that a ratio of the number of examination values included in the same level becomes identical between the examination items. At the same time, a boundary examination value giving a boundary of levels is determined, respectively. Then, the examination value distribution of examination items of each age division constituted by causing each record group having time-sequential data of the same examinee within the health check database to belong to the age division corresponding to the age when examined of the record is divided using the boundary examination values, thereby calculating the probability of transition of the examination value to a target level when the examination value corresponding to any arbitrary age division/examination item/level is given, on the basis of the number of examination values and the number of examination values transferred from the age division/examination item/level.

Description

The present invention relates to a transition destination level prediction method, a prediction apparatus, and a program for predicting a future level (e.g., an outline of a test value) from a current test value. Specifically, because it is information that represents the state of the living body from various viewpoints, by uniformly handling the inspection value of each inspection item having a correlation, it accurately reflects the correlation between the inspection items, The present invention relates to a transition destination level prediction method, a prediction device, and a program that enable highly accurate prediction.
Test items include, for example, BMI, systolic blood pressure, diastolic blood pressure, white blood cell count, red blood cell count, hemoglobin content, Ht (hematocrit), MCV (average red blood cell volume), MCH (average red blood cell hemoglobin content), MCHC (average red blood cell hemoglobin concentration). ), Platelets, GOT, GPT, γGTP, total cholesterol, neutral fat, HDL cholesterol, blood sugar as needed.

In conventional health guidance, occupational physicians and doctors in charge look at the health checkup results of the examinees, make decisions based on their own experience, and provide guidance on the future health status using linear regression equations from past test results. A method to predict and teach
However, the method based on the experience of the industrial physician and the attending physician has a problem that it requires a great deal of labor and cost. In addition, with the method based on the linear regression equation from past test results, although the trend can be read, it cannot be predicted taking into account both physiological changes as a group and individual characteristics. There is a problem that it can not stand the prediction of.
At present, there is no method for accurately predicting the future health state by utilizing the physiological characteristics inherent in the living body and the health state of each individual.

Japanese Patent Laid-Open No. 2006-343792 (Patent Document 1) describes a certain disease among patients who satisfy a first condition at a first time and satisfy a second condition at a second time. Create a number of rules that correlate the proportion (incidence rate) of patients who develop a according to disease and condition, and when the test value of the patient is entered, check which rule is satisfied, and The system which displays the disease and its incidence is described.
The first condition is, for example, gender: male, age: 40s, BMI ≧ 25, blood sugar level <125, snacking: etc., and the second condition corresponding to the first condition is Invariant items such as gender and age, or item values excluding predetermined items from the first condition, for example, BMI ≧ 30, blood glucose level <140, snacking: to do, and the like.
JP 2006-343792 A

In the system of Patent Document 1, since it is necessary for the creator to cover rules so that there are no omissions in consideration of various circumstances, not only is troublesome, but also dependent on the experience of the creator. The degree becomes large and a problem arises in prediction accuracy.
In addition, as a result, there are a large number of rules, so that a large load is applied to the arithmetic processing for extracting the corresponding rules from the input inspection value of the recipient.

The present invention makes it possible to make a highly accurate prediction as to how any current test value will change in the future by taking into account the physiological characteristics inherent in living organisms from both the group and individual perspectives. The purpose is to make it possible.
In addition, by utilizing the fact that the test value of each test item is information obtained from a unified body called a living body, it is possible to further improve the accuracy by correcting the above prediction using the correlation between test items. The purpose is to increase.

The configuration of the present invention is described in the following [1] to [13].
[1] Configuration 1
The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Boundary value calculation processing for dividing each of the predetermined number of levels so as to be the same and obtaining a boundary inspection value that gives a boundary of each level;
The test value distribution of each test item in each age section, each of which belongs to the age section corresponding to the age at the time of consultation of the record group of records having the same time series data of the same examinee in the medical examination database, Section level segmentation processing to segment using the boundary inspection value;
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. A transition probability calculation process that calculates based on the number of values and the number of test values that have transitioned from the arbitrary age section / test item / level to the calculation target level;
The transition destination level prediction method characterized by performing this.

The predetermined record group only needs to be able to constitute data that can uniformly represent the characteristics of a large number of people without being biased toward a specific group. For example, among the enormous amount of medical examination data accumulated over several years, 1 It is possible to employ a record group in which people who have been examined only once (only one examinee ID is extracted) are extracted.
Test items include, for example, BMI, systolic blood pressure, diastolic blood pressure, white blood cell count, red blood cell count, hemoglobin content, Ht (hematocrit), MCV (average red blood cell volume), MCH (average red blood cell hemoglobin content), MCHC (average red blood cell hemoglobin concentration). ), Platelets, GOT, GPT, γGTP, total cholesterol, neutral fat, HDL cholesterol, blood sugar as needed.
As a ratio between the number of sections and each level, for example, for each inspection item, 2.50% (level 1), 23.75% (level 2), 23.75% (level 3), 23.75% (level 4), 23.75% (level 5), and 2.50% (level 6) can be exemplified, but the method is not limited to this.
Record groups with the same time-series data for the same examinee include the same examinee's records across both adjacent age intervals, so that the aging of test values for each identical examinee To get. For example, it is a record group of examinees who have continued for 6 years and have taken a check once a year, and the age interval is set to 5 years.

[2] Configuration 2
In configuration 1,
In the boundary value calculation process, the test value distribution of each test item in each age section formed by allocating a predetermined group of records in the medical examination database to an age section corresponding to the age at the time of receiving the record, at the same level Divide into a predetermined number of levels so that the ratio of the number of inspection values included in each inspection item is the same, and obtain boundary inspection values that give the boundaries of each level,
In the section level classification process, a group of records having time-series data of the same examinee in the medical examination database is assigned to the same age section as that in the boundary value calculation process corresponding to the age at the time of visit of the record. The test value distribution of each test item in each age section is divided using the boundary test value, respectively.
The transition destination level prediction method characterized by this.
For example, in the boundary value calculation process and the section level classification process, the age sections are arranged as ˜19 years old, 20-24 years old, 25-29 years old, 30-34 years old,.

[3] Configuration 3
In Configuration 1 or Configuration 2,
In the transition probability calculation process, each level of the subsequent age section is set as a calculation target level, and the transition probability to each level is calculated. Hold as a level,
further,
Among the transition destination temporary levels or combinations of transition destination temporary levels obtained by the transition probability calculation processing from the inspection values of each inspection item of the prediction target person, the inspection items / levels classified in the boundary value calculation processing First data as an inspection item / level arbitrarily selected from the above or a combination of a plurality of inspection items / levels, and second data as a certain level of an inspection item different from the first data is greater than or equal to a predetermined value Check whether there is any one of the first data of any derivation rule defined as a combination of both data generated with probability, and if so, based on the second data derived using the derivation rule A transition destination correction process for correcting a transition destination temporary level in which the second data and the inspection item are the same,
The transition destination level prediction method characterized by performing this.
In the present specification, the probability that the second data will occur when the first data is assumed is appropriately referred to as “reliability”. For example, “80%” can be used as the predetermined value for giving the reliability threshold value, but is not limited thereto, and may be increased or decreased as appropriate.

[4] Configuration 4
In configuration 3,
In the transition destination correction process, the transition destination temporary level obtained by the transition probability calculation process from the inspection value of the inspection item / level specified by the user input or each inspection item of the prediction target person, or a combination thereof, Inspection data that is arbitrarily selected from the inspection items / levels divided in the boundary value calculation process, or a combination of a plurality of inspection items / levels, which is different from the first data. Check whether there is any data corresponding to the first data of any derivation rule defined as a combination of both data that causes the second data as a certain level of the item to occur with a probability equal to or higher than a predetermined value. , Based on the second data derived using the derivation rule, to correct the transition destination temporary level the same inspection item as the second data,
The transition destination level prediction method characterized by this.

[5] Configuration 5
In Configuration 3 or Configuration 4,
In the transition destination correction process, when second data having the same inspection item and different levels are derived by a plurality of derivation rules, the first of the derivation rules is determined based on the probability of generating the second data. 2 Select data,
The transition destination level prediction method characterized by this.
For example, in an example of a medical examination database, when the first data “Ht · 2” and “MCHC · 3” are assumed, the second data “hemoglobin amount · 2” is derived with a reliability of 100%. However, if the second data “hemochromosome · 1” is derived from another first data with a reliability of 83%, the second data with the higher reliability (“hemochromosome · 2”) is used. It is to choose.

[6] Configuration 6
The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Boundary value calculation means for dividing each of the predetermined number of levels so as to be the same, and obtaining a boundary inspection value that gives a boundary of each level;
The test value distribution of each test item in each age section, each of which belongs to the age section corresponding to the age at the time of consultation of the record group of records having the same time series data of the same examinee in the medical examination database, Section level classifying means for classifying using the boundary inspection value;
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. The calculation is based on the number of values and the number of each inspection value that has transitioned from each arbitrary age section / test item / level to each level as the calculation target level of the subsequent age section, and the transition probability among them. A transition probability calculating means for obtaining the maximum level and holding it as a transition destination temporary level;
From the inspection item / level classified by the boundary value calculation means in the transition destination temporary level or the combination of the transition destination temporary levels respectively obtained by the transition probability calculation means from the inspection value of each inspection item of the prediction target person First data as an arbitrarily selected inspection item / level or a combination of a plurality of inspection items / levels, and second data as a certain level of an inspection item different from the first data with a probability of a predetermined value or more Check whether there is any of the first data of any derivation rule defined as a combination of both data to be generated, and if so, based on the second data derived using the derivation rule Transition destination correcting means for correcting a transition destination temporary level having the same inspection item as the second data;
The transition destination level prediction apparatus characterized by having.

[7] Configuration 7
In configuration 6,
In the transition destination correction means, the transition destination temporary level obtained by the transition probability calculation means from the inspection item / level specified by the user input or the inspection value of each inspection item of the prediction target person, or a combination thereof, Inspection data that is arbitrarily selected from the inspection items / levels divided by the boundary value calculation means, or a combination of a plurality of inspection items / levels, which is different from the first data. Check whether there is any data corresponding to the first data of any derivation rule defined as a combination of both data that causes the second data as a certain level of the item to occur with a probability equal to or higher than a predetermined value. , Based on the second data derived using the derivation rule, to correct the transition destination temporary level the same inspection item as the second data,
The transition destination level prediction apparatus characterized by this.

[8] Configuration 8
The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Boundary value calculation means for dividing each of the predetermined number of levels so as to be the same, and obtaining a boundary inspection value that gives a boundary of each level;
The test value distribution of each test item in each age section, each of which belongs to the age section corresponding to the age at the time of consultation of the record group of records having the same time series data of the same examinee in the medical examination database, Section level classifying means for classifying using the boundary inspection value;
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. A transition probability calculation means for calculating based on the number of values and the number of test values that have transitioned from the arbitrary age section / test item / level to the level to be calculated;
The transition destination level prediction apparatus characterized by having.

[9] Configuration 9
In any of Configuration 6 to Configuration 8,
The boundary value calculating means assigns the test value distribution of each test item in each age section to which the predetermined record group in the medical examination database belongs to the age section corresponding to the age at the time of receiving the record at the same level. Divide into a predetermined number of levels so that the ratio of the number of inspection values included in each inspection item is the same, and obtain boundary inspection values that give the boundaries of each level,
The section level classification means assigns a group of records having time series data of the same examinee in the medical examination database to the same age section as that of the boundary value calculation means corresponding to the age at the time of visit of the record. The test value distribution of each test item in each age section is divided using the boundary test value, respectively.
The transition destination level prediction apparatus characterized by this.

[10] Configuration 10
The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Using a boundary test value that gives a boundary of each level determined by dividing each predetermined number of levels so as to be the same, a record group having time series data of the same examinee in the medical examination database Separate the test value distribution of each test item in each age section that belongs to the age section corresponding to the age at the time of consultation of the record,
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. Calculate based on the number of values and the number of test values that have transitioned from the arbitrary age section / test item / level to the target level for calculation,
The transition destination level prediction method characterized by this.
This is a method in which the section level division process of configuration 1 is executed using data having the processing result of the boundary value calculation process of configuration 1.

[11] Configuration 11
The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Using a boundary test value that gives a boundary of each level determined by dividing each predetermined number of levels so as to be the same, a record group having time series data of the same examinee in the medical examination database Section level classification means for classifying the test value distribution of each test item of each age section each belonging to the age section corresponding to the age at the time of consultation of the record,
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. A transition probability calculation means for calculating based on the number of values and the number of test values that have transitioned from the arbitrary age section / test item / level to the level to be calculated;
The transition destination level prediction apparatus characterized by having.

[12] Configuration 12
The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Using a boundary test value that gives a boundary of each level determined by dividing each predetermined number of levels so as to be the same, a record group having time series data of the same examinee in the medical examination database Using the data obtained by classifying the test value distribution of each test item of each age section that belongs to the age section corresponding to the age at the time of consultation of the record,
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. Calculate based on the number of values and the number of test values that have transitioned from the arbitrary age section / test item / level to the target level for calculation,
The transition destination level prediction method characterized by this.

[5] Configuration 5
A program for causing a computer to function as a transition destination level prediction apparatus according to any one of configurations 6, 7, 8, 9, and 11.

In the configuration 1, the ratio of the number of test values included in the same level of the test value distribution of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is Boundary value calculation processing for obtaining boundary test values for dividing each item into a predetermined number of levels so as to be the same between the items and giving a boundary of each level, and time series data of the same examinee in the medical examination database Section level classification processing for classifying the test value distribution of each test item in each age section, which is made by belonging to each age group corresponding to the age at the time of consultation of the record, using the boundary test value, and an arbitrary level If a test value corresponding to an age section, test item, or level is given, the probability of the test value transitioning to the calculation target level of the subsequent age section is set to the arbitrary age group. -Transition destination level prediction that executes a transition probability calculation process that is calculated based on the number of test values belonging to the test item / level and the number of test values that have transitioned from the arbitrary age section / test item / level to the calculation target level This method considers the physiological characteristics inherent to the living body from both the group and individual level, so the probability of how the level of any current test value will change in the future is calculated accurately. it can.
In the configuration 2, in the configuration 1, the boundary value calculation process is performed for each examination in each age section in which a predetermined record group in the medical examination database belongs to an age section corresponding to the age at the time of the medical checkup of the record. Dividing the inspection value distribution of the items into a predetermined number of levels so that the ratio of the number of inspection values included in the same level is the same among each inspection item, and obtaining boundary inspection values that give the boundaries of each level, respectively The section level classification process is performed by causing a group of records having time-series data of the same examinee in the medical examination database to belong to the same age section as that in the boundary value calculation process corresponding to the age at the visit of the record. This is a transition destination level prediction method that divides the inspection value distribution of each inspection item in each age section using the boundary inspection value, and is the same as the age section for calculating the boundary value Due to the application of the old section to the section level bifurcation, it can be further accurately calculated from structure 1.

In the configuration 3, in the configuration 1 or the configuration 2, the transition probability calculation process sets each level of the subsequent age section as a calculation target level, calculates a transition probability to each level, and transits among them. A level with the highest probability is obtained and held as a transition destination temporary level, and further, a transition destination temporary level or a combination of transition destination temporary levels respectively obtained by the transition probability calculation process from the inspection value of each inspection item of the prediction target person A first data as an inspection item / level or a combination of a plurality of inspection items / levels arbitrarily selected from the inspection items / levels divided in the boundary value calculation process, and the first data Corresponds to the first data of any derivation rule defined as a combination of both data that causes the second data as a certain level of different inspection items to occur with a probability of a predetermined value or more. The transition for executing the transition destination correction process for correcting the transition destination provisional level where the second data and the inspection item are the same based on the second data derived using the derivation rule. This is a first-level prediction method, and since the test value of each test item is information obtained from a unified body called a living body, the prediction of the configuration 1 or 2 is corrected by using the correlation, so the accuracy is further improved. Can be increased.
In the configuration 4, the transition destination correction processing in the configuration 3 is performed by the transition destination obtained by the transition probability calculation processing from the inspection item / level specified by the user input or the inspection value of each inspection item of the prediction target person. First data as an inspection item / level or a combination of a plurality of inspection items / levels arbitrarily selected from the inspection items / levels divided in the boundary value calculation process in the temporary level or a combination thereof What corresponds to the first data of any derivation rule defined as a combination of both data that causes the second data as a certain level of the inspection item different from the first data to occur with a probability equal to or higher than a predetermined value If there is, the transition destination level for correcting the transition destination provisional level in which the second data and the inspection item are the same based on the second data derived using the derivation rule. Because it is Le prediction method, it is possible to perform accurate prediction regard test item-level specified arbitrarily by the user input.
In the configuration 5, in the configuration 3 or the configuration 4, the transition destination correction process generates the second data when the second data having the same inspection item and different levels are derived by a plurality of derivation rules, respectively. Since the transition destination level prediction method selects the second data of any one derivation rule based on the probability, the second data can be appropriately selected.

The configuration 6 can provide an apparatus that executes the processing of the configuration 3 that receives the configuration 1.
The configuration 7 can provide an apparatus that executes the processing of the configuration 4 that receives the configuration 3 that receives the configuration 1.
The configuration 8 can provide an apparatus that executes the processing of the configuration 1.
The configuration 9 can provide a device that executes the processing of the configuration 2, the configuration 3 that receives the configuration 2, and the configuration 4 that receives the configuration 3 that receives the configuration 2.
The configuration 10 can provide a method of obtaining the same effect as the configuration 1 by using data having the processing result of the boundary value calculation process of the configuration 1.
The configuration 11 can provide an apparatus that executes the processing of the configuration 10.
The configuration 12 can provide a method of obtaining the same effect as the configuration 1 by using data having the processing result of the boundary value calculation processing and the section level division processing of the configuration 1.
The configuration 13 can provide a program for causing a computer to function as the transition destination level prediction apparatus of any of the configurations 6, 7, 8, 9, or 11.

The flowchart which shows the procedure of medical examination data processing & prediction processing. The block diagram (a) which shows the structure of the apparatus of embodiment, and explanatory drawing (b) of the technique of a level division. Explanatory drawing (a) which shows the example of a structure of a medical examination database, and explanatory drawing (b) which shows the example which levels a test value. Explanatory drawing which illustrates the probability distribution according to the age range of a test value, and a transition probability about the case of male and triglyceride (before derivation rule application). Explanatory drawing (a) which shows the extraction example of a derivation rule, and explanatory drawing (b) which shows the example which replaces the level of a transition destination with the 2nd data of a derivation rule. The explanatory diagram of FIG. 4 shows after the derivation rule is applied. Explanatory drawing which shows the change of a display at the time of selecting arbitrary age sections and levels (45-49 years old and level 6) in explanatory drawing of FIG. Change the display when any age section / level (45-49 years old / level 6) is selected in the explanatory diagram of FIG. 4, part of the section division map is hidden, and the current prediction is also shown Explanatory drawing displayed and shown.

Embodiments of the present invention will be described with reference to the drawings.
The method / apparatus of the embodiment is realized in the computer system shown in FIG. That is, it is realized by the control device 10 reading a predetermined program from the ROM 13 or the storage device 11 as necessary and executing the procedure shown in FIG.

  A medical examination database having a quantity of medical examination data that can be statistically processed is stored in the storage device 11 constituted by a hard disk or SSD. Various applications such as an application for realizing the medical examination data processing function of the present application may be stored as necessary.

A configuration example of the medical examination database is shown in FIG.
In the example shown in the drawing, 18 examination items (BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), white blood cell count, red blood cell count, hemoglobin amount (Hb) are associated with a unique medical examinee ID and date of medical examination. ), Hematocrit (Ht), MCV, MCH, MCHC, platelets, GOT, GPT, γGTP, total cholesterol, neutral fat, HDL cholesterol (in the figure, abbreviated as HDL), blood glucose at any time). These inspection items are examples, and may be increased or decreased as appropriate, such as adding other inspection items. In another table (not shown), attributes of the examinee such as age and sex are recorded in association with a common examinee ID.

The control device 10 has a known configuration having a CPU and the like.
In the example of FIG. 2A, the storage device (hard disk) 11 is connected to the control device 10, but may be connected via a LAN or the like, for example. Also, the input device is not limited to the keyboard / mouse shown in the figure. For example, the configuration is such that a medical examination result recorded on an IC card is read and stored in a medical examination database, or input via a LAN (or the Internet). A configuration may be used in which the health check results to be stored in the health check database. The same applies to the input of the prediction target person's data (inspection value of each inspection item). Further, the output device is not limited to the display, and may be a printer or a speaker. In short, the hardware configuration is arbitrary as long as the configuration of FIG. 1 can be executed.

A procedure for realizing the functions of the apparatus according to the embodiment will be described with reference to FIG.
First, the data of the record which has the item value of each test | inspection item shown to Fig.3 (a) from a medical examination database is acquired. Moreover, the attribute (age / gender) data of each examinee associated with the examinee ID of each record is also acquired from a table (not shown).
In this example, 1-time medical examination data (number of records: 64194 cases) and 6-time medical examination data (total number of examinee IDs: 12099) are used as the medical examination data.

The one-time consultation data is data in which a record of the patient ID exists only once in any one of the six years in which the data is accumulated.
The first visit data is classified by age section and sex in the level classification process (S21), and is classified into a predetermined number of levels for each examination item (= discretized), and further, boundary inspection at each level A value is calculated. Each calculated boundary inspection value is used for leveling (= discretization) of the 6-time consultation data. Moreover, the single consultation data classified according to age section and sex and leveled for each examination item is provided to the rule extraction process (S41). Details of the rule extraction process (S41) will be described later.

The 6-time medical examination data is data in which one record of the medical examiner ID exists for each of the six years in which the data is accumulated. In addition, when there are a plurality of records having the same “year” of the consultation date with the same examiner ID, the data of the examinee ID is excluded.
The 6th consultation data is classified by age section and sex in the level classification process (S21), and using the boundary test values of each level calculated by leveling in the same age section of the 1 consultation data, Each inspection item is leveled (= discretized). Further, the 6-time consultation data classified by age section and sex and leveled for each examination item is subjected to a transition probability calculation process (S31). Details of the transition probability calculation process (S31) will be described later.

  Note that the “grouping of the 1-time consultation data and the 6-time consultation data” in step S13 can be performed by checking the ID of the patient and the date of reception.

  In step S21, as described above, the first visit data is classified by age section and sex, and each classification is classified into a predetermined number of levels for each examination item. In addition, the 6 visit data is classified by age section and sex, and each classification is leveled using the corresponding age section / sex / level boundary test values obtained by leveling the 1 visit data. .

  As shown in Fig. 2 (b), the level of any age section / sex / test item in the single consultation data starts from the test value of the minimum value of the age section / sex / test item. , Inspection values included up to 2.50%, inspection values included up to 26.25%, inspection values included up to 50.00%, inspection values included up to 73.75%, inspection values included up to 97.50% The inspection values included up to 100.00% are obtained by obtaining each. 2. 50% or less is an abnormal value on the lower test value side, and more than 97.50% is an abnormal value on the higher side. Moreover, it is larger than 2.50% and 97.50% or less is the range made into a normal value. As the age section, in this example, the increment of 5 years is adopted. That is, from 5 to 19 years old, 20 to 24 years old, 25 to 29 years old, 30 to 35 years old, etc. are adopted in increments of 5 years (see FIG. 4).

  Each boundary test value obtained by leveling the data of the first visit is used for leveling the same age section / sex / test item of the data for the sixth checkup. For example, the 40-44-year-old male visit data and the level of border test values obtained by leveling triglycerides in males are converted to the level of 40-44-year-old male males and triglyceride data obtained in 6 visits. Used for. In this way, the age section, sex, and test items are aligned, so even if there is a deviation in the standard values of test values depending on age or gender, a large amount of data can be obtained from 6 times of consultation data, which has a relatively small amount of data. Can be leveled with good accuracy equivalent to single-visit data.

  In this way, the test values of each age section, each sex, and each test item of the 1st consultation data and 6th consultation data are all test values for the age section, sex, and test items of the same level. After dividing into a predetermined number of levels (six in this example) so that the ratio to the number is the same among the test items, each test value of both consultation data is then set to the corresponding level. Create data replaced with (discrete value). Thereby, it becomes possible to handle each test value of each age section, each sex, and each test item of both consultation data uniformly by “test item / level”. An example in which the inspection value is replaced with the corresponding level is shown in FIG.

  Furthermore, regarding the first consultation data, the probability distribution of each examination item by age section and sex is obtained, and a section division diagram expressed by levels (discrete values) is created. The section division diagram is a diagram in which each age section is classified by level and arranged in order of age sections. FIG. 4 exemplifies a section division diagram created by obtaining a probability distribution of each level of each age section in the case of male / neutral fat. Level 1 is the low inspection value side and level 6 is the high side. In FIG. 4, although intervals are provided between adjacent age sections, they may be in close contact.

In this example, the section division diagram as shown in FIG. 4 is output to the display, and the transition probability and the transition destination are displayed on the section division diagram. That is, a transition mode diagram is displayed. For example, an arrow obtained by subtracting the magnitude of the transition probability when transitioning from a certain level in a certain age section to each level in the subsequent age section from a certain level in the certain age section to each level in the subsequent age section You may make it express with the thickness of. Alternatively, it may be represented by the magnitude of the brightness of the arrow or the line type (solid line, broken line, dotted line, etc.) of the arrow, and may be displayed in consideration of the difference in thickness. In FIG. 4, the vertical axis is a probability distribution, but the probability distribution on the vertical axis may be converted into a test value and displayed so that the actual test value can be understood. Good. That is, it is needless to say that the display may be appropriately improved so as to provide a user-friendly display.
Details of the transition destination display will be described later.

In step S31, a transition probability calculation process is executed using the 6th consultation data.
As described above, in the six-time medical examination data, there is one record having the same medical examiner ID in each of the six years in which the data is accumulated. Therefore, when an age section is set in increments of 5 years as in this example, the adjacent age sections (eg, 40 to 44 years, 45 to 49 years) always include data of the same examinee. . For this reason, when using the 6-time consultation data, it is possible to accurately estimate the aging of each person's test value, and consequently, transition from any level in any age section to each level in the subsequent age section. It is possible to estimate the probability of performing with high accuracy.

The transition probabilities are calculated for each test item, age section, and level for each data item belonging to a certain test item, age section, and level. This is done by counting the number of data to which it belongs.
For example, in the case of triglycerides / male, there are 213 data (not shown) of 6-time examination data belonging to 40 to 44 years old / level 4, but 41 cases (= 19.2 in the age section 45 to 49 years old). %) Is Level 6, 64 (= 30.0%) is Level 5, 51 (= 23.9%) is Level 4, 31 (= 14.6%) is Level 3, 26 (= 12) .2%) belongs to level 2, and 0 cases (= 0%) belong to level 1. As a result, estimation as shown in the figure becomes possible.
Based on the counting result, in step S31, the transition probability to each level of the subsequent age section starting from an arbitrary age section / level of each inspection item is calculated.

In step S41, a rule extraction process is executed. As the data, single visit data classified by age section and sex in the level classification process (S21) and classified into a predetermined number (six) levels for each examination item are used.
In the rule extraction process, when a certain inspection item / level or a combination of two or more inspection items / levels (first data) is assumed, there is a difference from any inspection item of the first data based on the assumption. When the probability that a certain level of the inspection item (inspection item / level of the second data) occurs is equal to or higher than a predetermined value (for example, 80%), the combination of the first data and the second data is determined as the first data. This is extracted as a derivation rule for deriving the second data from the data.

  For example, as shown in FIG. 5A, “the probability that the lowest blood pressure · 1 will occur when the highest blood pressure · 1 is assumed is 83.33% (confidence; see))”, which is a predetermined value (80 %) Or more. For this reason, a derivation rule of “maximum blood pressure · 1 as first data and minimum blood pressure · 1 as second data” is extracted. Similarly, when “Ht · 2 and MCHC · 3 are assumed, the probability of occurrence of hemoglobin amount · 2 is 100%, which is equal to or higher than a predetermined value (80%. Therefore,“ Ht · 2 and MCHC · 3 as first data and hemoglobin amount · 2 as second data ”is derived.

  By appropriately using such a derivation rule, the inspection value (level) of another inspection item having correlation is accurately estimated or corrected from the inspection value (level) of the inspection item having correlation or a combination thereof. It becomes possible. For example, it is possible to accurately correct the magnitude relationship of the transition probability to each subsequent level calculated in the transition probability calculation process (S31) (eg, which level has the highest transition probability). It becomes.

In the rule extraction process, combinations that are not possible as the first data, that is, combinations having the same inspection item and different levels (eg, Ht · 2 and Ht · 3) are considered as candidates for the first data. Are excluded in advance. As a result, it is possible to prevent the occurrence probability from being wasted and to reduce the load on the computer.
In addition, even when the probability that the first data and the second data occur at the same time (see support) is extremely small (eg, smaller than 3%), it is excluded from the calculation target of the occurrence probability and the load is reduced. .

In step S51, a prediction process is executed.
First, data of a prediction target person is acquired. As this data, each test value of the same test item (refer to Drawing 3 (a)) and a candidate for prediction (age, sex) can be mentioned as a medical examination database. Note that not all inspection values for the same inspection items as the medical examination database may be provided. That is, only some inspection values may be used. Further, the prediction target person may be a real examinee or a virtual examinee.

  The prediction target person data may be input from an input device such as a keyboard or a mouse, or may be configured to read data such as an IC card. Moreover, the structure which acquires the data input via LAN and also the internet may be sufficient. Alternatively, a configuration may be adopted in which prediction target persons are read out in a predetermined order from a list of medical examiners recorded in advance on a hard disk or the like and acquired in order.

  Next, the inspection value of each inspection item acquired in this way is leveled. That is, each is replaced with the corresponding level. As the boundary inspection value for leveling, the boundary inspection value corresponding to the age and sex of the prediction target person is used among the boundary inspection values calculated based on the data received once in step S21 (level classification processing). .

  Next, the probability of transition from the current level to each level of the subsequent age section is acquired. The current level is a level to which the test value of the prediction target person belongs. The subsequent age section is an age section to which the age of the prediction target person five years later belongs. As the transition probability to each level of the subsequent age section, each value calculated based on the 6th consultation data in step S31 (transition probability calculation processing) is acquired. Further, the transition destination level having the maximum transition probability among the transition probabilities is held on the memory 12 as a transition destination temporary level.

The transition probability to each level in the subsequent age section will be described.
For example, when the age of the prediction target is 42 years (age section: 40 to 44 years) and the level corresponding to the test value of the test item “neutral fat” is “4”, it is indicated by a black circle in FIG. The current position is the current level. Starting from this position, the probability of transition to each level in the subsequent age section (45 to 49 years old) is 19.2% for level 6, 30.3% for level 5, 23.9% for level 4 and level. 3 is 14.6%, level 2 is 12.2%, and level 1 is 0.0%. These are calculated on the basis of the count values in the transition probability calculation process in step S31 and are stored in the memory 12.

In this case, the transition probability has the maximum transition rate of 30.0% in the case of transition to level 5, so this level 5 is held as a transition destination temporary level. In FIG. 4, the arrow toward level 5 with the maximum transition probability is shown as the thickest. That is, in FIG. 4, the thickest solid line arrow, the thick solid line arrow, the thin solid line arrow, the thin broken line arrow, the thin dotted line arrow, and the thin two-dot chain line arrow are shown in descending order of the transition probability. As the starting point of the arrow, for example, the center position of the level can be set, but is not limited thereto. For example, as described above, since the probability distribution and / or the actual inspection value can be displayed on the vertical axis in FIG. 4, the height of the inspection value of the prediction target person may be set as the starting point.
The display of FIG. 4 is as described above, but in FIGS. 6 to 8 to be described later, the line type and thickness of the arrow, the position indicated by the arrow, or the like is appropriately changed according to the correction using the rule. Has been.

  When the transition destination level (transition destination temporary level) having the maximum transition probability is determined, correction based on the derivation rule is performed next. That is, the transition destination temporary level (the transition destination level having the maximum transition probability) obtained based on each inspection value of the prediction target person or the combination of the transition destination temporary levels corresponds to the first data of the derivation rule. When there is something, the second data (inspection item / level) derived using the derivation rule is corrected to replace the transition destination provisional level in which the second data and the inspection item are the same.

  For example, in the case where ““ Ht · 2 ”,“ MCHC · 3 ”, and“ hemoglobin amount · 3 ”are determined as the transition destination temporary level based on the test value of each test item of the prediction target person, FIG. As shown in a), there is a derivation rule with ““ Ht · 2 ”and“ MCHC · 3 ”as the first data and“ hemoglobin amount · 2 ”as the second data. For this reason, in this case, “hemoglobin amount · 3” held as the transition destination provisional level is replaced with the second data “hemoglobin amount · 2”. This state is shown in FIG.

Such correction will be described with reference to a section diagram of the neutral fat.
FIG. 6 shows a state in which the thickness of the arrow is changed according to the derivation rule as originally shown in FIG. That is, the maximum transition probability level (transition destination provisional level) from the level 4 of the triglyceride age section 40 to 44 years to the subsequent age section is the level 5 as shown in FIG. There is a rule in which “neutral fat · 4” is the second data, and the first data of the derivation rule is one or more transition destination temporary levels obtained based on each test value of the prediction target person Therefore, “neutral fat 5” is replaced with “neutral fat 4”, and as a result, the thickness of the arrow is changed as shown in FIG.

In the above description, the case where the display is switched from FIG. 4 to FIG. 6 has been described. However, in the case where the correction based on the derivation rule is automatically executed immediately after the transition destination temporary level is calculated. The display of FIG. 4 is not actually performed, and the display of FIG. 6 is immediately performed. Further, the switching from FIG. 4 to FIG. 6 may be configured to be performed in response to a user instruction input.
Moreover, the display of FIG. 4 and FIG. 6 shows an example. For example, the thickness and line type of the arrow in FIG. 6 are the same as those in FIG. 4, and the level changed by the derivation rule is highlighted instead. (For example, highlight display) or the like may be shown to the user.

Examples of the display mode corresponding to the transition probability and the mode of displaying the correction based on the derivation rule are as follows. That is,
As a display mode according to the transition probability, for example,
(A) A display mode in which an arrow is drawn from the calculation starting point to the transition destination level, and the thickness is increased as the transition probability increases.
(B) A display mode in which the line type of the arrow is represented by a solid line, a broken line, a dotted line, a chain line, or the like according to the transition probability.
(C) A display mode in which the brightness and color of the arrows are more conspicuous as the transition probability increases.
(D) A display mode in which the display of the level area of the transition destination is changed according to the transition probability. For example, a display mode in which, for example, a highlight display is made so that the level of the transition probability is higher.
(E) A display mode in which numerical values of transition probabilities are displayed alongside. Further, in this case, a display mode in which typefaces and points are changed according to the magnitude of the transition probability.
Can be illustrated.

Moreover, as a predetermined aspect incorporating the correction, for example,
(1) The transition destination provisional level (the level with the highest transition probability) and the level (modified level) indicated by the second data of the derivation rule are clearly distinguished and displayed so that the meaning of both can be understood. A mode of (highlighted).
(2) A mode in which highlighting of the transition destination provisional level (the level having the maximum transition probability) is stopped, and the level (modified level) indicated by the second data of the derivation rule is highlighted as a high transition probability level.
(3) In the above (2), the transition probability is high, but the fact that the transition probability is different from the maximum level (transition destination temporary level) is also displayed.
(4) In the above (2), not only the highlighting of the transition destination temporary level (the level with the highest transition probability) is stopped, but also the display based on the transition probability to another level is stopped, and the second derivation rule A mode in which only the level indicated by the data (the level after correction) is highlighted as a level having a high probability of transition.
(5) If the transition destination provisional level (the level with the highest transition probability) and the level indicated by the second data of the derivation rule (the corrected level) are adjacent, the boundary portion is set as the arrow destination. To display.
Etc. can be illustrated.
Note that a display mode other than the above may be used as the display mode corresponding to the transition probability and the predetermined mode incorporating the correction.

In the description of the correction based on the derivation rule, the corrected “inspection item / level” may constitute the first data of another derivation rule or a part thereof. In that case, the transition destination temporary level of another inspection item is replaced by the second data derived by the derivation rule, and the fluctuation has spread one after another. There may be a situation where the “test item / level (the amount of hemoglobin in FIG. 5B) / 2” is replaced again. In order to prevent such an indefinite state due to the circulation phenomenon, this device is configured so that the “inspection item / level” replaced by the second data of the derivation rule is not used as the first data of the derivation rule. ing.
Instead of this, for example, a configuration such as “stop applying the derivation rule when replacement is attempted again” may be employed.

  Further, in this apparatus, when any “inspection item / level” is selected, the system is configured to process the “inspection item / level” as a component of the first data for the inspection item. It is composed. In other words, the system is configured to process the inspection item by regarding the “inspection item / level” as a transition destination temporary level.

This will be described with reference to FIGS. 4 and 7. FIG.
In FIG. 4, the transition destination temporary level is “level 5”. At this time, for example, when “level 6” is selected as shown in the figure, the “level 6” is used for the test item “neutral fat” in FIG. 4 and the transitions are made for other test items. Derivation rules are applied using the preliminary level. Furthermore, starting from the “level 6”, the transition probabilities to the respective levels of the subsequent age sections are respectively acquired, and as shown in FIG. 7, the thickness and line type corresponding to the acquired transition probabilities are obtained. An arrow indicates the transition to each level.
For this reason, for example, at present, the user has the highest probability of transitioning to “level 5” five years ahead, but has been transitioned to “level 6”, for example, as a result of repeated non-consumption. Then, it is possible to easily understand how the transition proceeds thereafter.

FIG. 8 shows an example in which FIG. 7 is displayed differently.
In FIG. 8, a part of the section division diagram is not displayed.
Further, in FIG. 8, the transition to the selected “level 6” is indicated by a two-dot chain thick arrow as an assumed prediction (maximum possibility), and the transition destination from the “level 6” is also indicated by a two-dot chain thick line. It is indicated by an arrow (maximum possibility) and a two-dot chain line arrow (next possibility).
Further, in FIG. 8, the transition probability “level 5” was the maximum from the inspection result of each inspection item of the prediction target person, but “level 4” was derived in the modification by the derivation rule, so the boundary between both levels Is set as the arrow destination.

Such an output can be output not only by a display but also by a printer, for example. Further, it may be configured to transmit to the inquiry source terminal device via the LAN or the Internet.
As a result of calculating the transition probability with the apparatus of the present application and applying the result corrected by the derivation rule to the data of a large number of medical examiners, it was confirmed that good accuracy was obtained.

10 Control device (equipped with CPU)
11 Storage devices (hard disks, etc.)
12 Storage device (RAM)
13 Storage device (ROM)
16 Input devices (keyboard, mouse, etc.)
17 Output device (display, etc.)
18 LAN

Claims (13)

The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Boundary value calculation processing for dividing each of the predetermined number of levels so as to be the same and obtaining a boundary inspection value that gives a boundary of each level;
The test value distribution of each test item in each age section, each of which belongs to the age section corresponding to the age at the time of consultation of the record group of records having the same time series data of the same examinee in the medical examination database, Section level segmentation processing to segment using the boundary inspection value;
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. A transition probability calculation process that calculates based on the number of values and the number of test values that have transitioned from the arbitrary age section / test item / level to the calculation target level;
The transition destination level prediction method characterized by performing this. In claim 1,
In the boundary value calculation process, the test value distribution of each test item in each age section formed by allocating a predetermined group of records in the medical examination database to an age section corresponding to the age at the time of receiving the record, at the same level Divide into a predetermined number of levels so that the ratio of the number of inspection values included in each inspection item is the same, and obtain boundary inspection values that give the boundaries of each level,
In the section level classification process, a group of records having time-series data of the same examinee in the medical examination database is assigned to the same age section as that in the boundary value calculation process corresponding to the age at the time of visit of the record. The test value distribution of each test item in each age section is divided using the boundary test value, respectively.
The transition destination level prediction method characterized by this. In claim 1 or claim 2,
In the transition probability calculation process, each level of the subsequent age section is set as a calculation target level, and the transition probability to each level is calculated. Hold as a level,
further,
Among the transition destination temporary levels or combinations of transition destination temporary levels obtained by the transition probability calculation processing from the inspection values of each inspection item of the prediction target person, the inspection items / levels classified in the boundary value calculation processing First data as an inspection item / level arbitrarily selected from the above or a combination of a plurality of inspection items / levels, and second data as a certain level of an inspection item different from the first data is greater than or equal to a predetermined value Check whether there is any one of the first data of any derivation rule defined as a combination of both data generated with probability, and if so, based on the second data derived using the derivation rule The transition destination correction process for correcting the transition destination temporary level in which the second data and the inspection item are the same,
The transition destination level prediction method characterized by performing this. In claim 3,
In the transition destination correction process, the transition destination temporary level obtained by the transition probability calculation process from the inspection value of the inspection item / level specified by the user input or each inspection item of the prediction target person, or a combination thereof, Inspection data that is arbitrarily selected from the inspection items / levels divided in the boundary value calculation process, or a combination of a plurality of inspection items / levels, which is different from the first data. Check whether there is any data corresponding to the first data of any derivation rule defined as a combination of both data that causes the second data as a certain level of the item to occur with a probability equal to or higher than a predetermined value. , Based on the second data derived using the derivation rule, to correct the transition destination temporary level the same inspection item as the second data,
The transition destination level prediction method characterized by this. In claim 3 or claim 4,
In the transition destination correction process, when second data having the same inspection item and different levels are derived by a plurality of derivation rules, the first of the derivation rules is determined based on the probability of generating the second data. 2 Select data,
The transition destination level prediction method characterized by this. The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Boundary value calculation means for dividing each of the predetermined number of levels so as to be the same, and obtaining a boundary inspection value that gives a boundary of each level;
The test value distribution of each test item in each age section, each of which belongs to the age section corresponding to the age at the time of consultation of the record group of records having the same time series data of the same examinee in the medical examination database, Section level classifying means for classifying using the boundary inspection value;
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. The calculation is based on the number of values and the number of each inspection value that has transitioned from each arbitrary age section / test item / level to each level as the calculation target level of the subsequent age section, and the transition probability among them. A transition probability calculating means for obtaining the maximum level and holding it as a transition destination temporary level;
From the inspection item / level classified by the boundary value calculation means in the transition destination temporary level or the combination of the transition destination temporary levels respectively obtained by the transition probability calculation means from the inspection value of each inspection item of the prediction target person First data as an arbitrarily selected inspection item / level or a combination of a plurality of inspection items / levels, and second data as a certain level of an inspection item different from the first data with a probability of a predetermined value or more Check whether there is any of the first data of any derivation rule defined as a combination of both data to be generated, and if so, based on the second data derived using the derivation rule Transition destination correcting means for correcting a transition destination temporary level having the same inspection item as the second data;
The transition destination level prediction apparatus characterized by having. In claim 6,
In the transition destination correction means, the transition destination temporary level obtained by the transition probability calculation means from the inspection item / level specified by the user input or the inspection value of each inspection item of the prediction target person, or a combination thereof, Inspection data that is arbitrarily selected from the inspection items / levels divided by the boundary value calculation means, or a combination of a plurality of inspection items / levels, which is different from the first data. Check whether there is any data corresponding to the first data of any derivation rule defined as a combination of both data that causes the second data as a certain level of the item to occur with a probability equal to or higher than a predetermined value. , Based on the second data derived using the derivation rule, to correct the transition destination temporary level the same inspection item as the second data,
The transition destination level prediction apparatus characterized by this. The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Boundary value calculation means for dividing each of the predetermined number of levels so as to be the same, and obtaining a boundary inspection value that gives a boundary of each level;
The test value distribution of each test item in each age section, each of which belongs to the age section corresponding to the age at the time of consultation of the record group of records having the same time series data of the same examinee in the medical examination database, Section level classifying means for classifying using the boundary inspection value;
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. A transition probability calculation means for calculating based on the number of values and the number of test values that have transitioned from the arbitrary age section / test item / level to the level to be calculated;
The transition destination level prediction apparatus characterized by having. In any one of Claims 6-8,
The boundary value calculating means assigns the test value distribution of each test item in each age section to which the predetermined record group in the medical examination database belongs to the age section corresponding to the age at the time of receiving the record at the same level. Divide into a predetermined number of levels so that the ratio of the number of inspection values included in each inspection item is the same, and obtain boundary inspection values that give the boundaries of each level,
The section level classification means assigns a group of records having time series data of the same examinee in the medical examination database to the same age section as that of the boundary value calculation means corresponding to the age at the time of visit of the record. The test value distribution of each test item in each age section is divided using the boundary test value, respectively.
The transition destination level prediction apparatus characterized by this. The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Using a boundary test value that gives a boundary of each level determined by dividing each predetermined number of levels so as to be the same, a record group having time series data of the same examinee in the medical examination database Separate the test value distribution of each test item in each age section that belongs to the age section corresponding to the age at the time of consultation of the record,
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. Calculate based on the number of values and the number of test values that have transitioned from the arbitrary age section / test item / level to the target level for calculation,
The transition destination level prediction method characterized by this. The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Using a boundary test value that gives a boundary of each level determined by dividing each predetermined number of levels so as to be the same, a record group having time series data of the same examinee in the medical examination database Section level classification means for classifying the test value distribution of each test item of each age section each belonging to the age section corresponding to the age at the time of consultation of the record,
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. A transition probability calculation means for calculating based on the number of values and the number of test values that have transitioned from the arbitrary age section / test item / level to the level to be calculated;
The transition destination level prediction apparatus characterized by having. The distribution of the test values of each test item in the predetermined record group in the medical examination database having the amount of data that can be statistically processed for each medical test item is the ratio of the number of test values included in the same level between the test items. Using a boundary test value that gives a boundary of each level determined by dividing each predetermined number of levels so as to be the same, a record group having time series data of the same examinee in the medical examination database Using the data obtained by classifying the test value distribution of each test item of each age section that belongs to the age section corresponding to the age at the time of consultation of the record,
When a test value corresponding to an arbitrary age section, test item, or level is given, the probability that the test value transitions to the calculation target level of the subsequent age section is the test that belongs to the arbitrary age section, test item, or level. Calculate based on the number of values and the number of test values that have transitioned from the arbitrary age section / test item / level to the target level for calculation,
The transition destination level prediction method characterized by this.   A program for causing a computer to function as the transition destination level prediction apparatus according to claim 6, 7, 8, 9, or 11.

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