HEALTH SUPPORT APPARATUS, HEALTH SUPPORT METHOD, AND RECORDING MEDIUM RECORDING HEALTH SUPPORT PROGRAM

A health support apparatus includes a processor including hardware. The processor accepts input of a first factor that is directly controlled by improving a lifestyle habit of a person subjected to a medical checkup and a second factor that is not directly controlled by improving a lifestyle habit of the person. The processor generates a lifestyle habit combination pattern. The processor calculates change amounts of the first factor and the second factor corresponding to the combination pattern. The processor predicts a disease risk value representing a disease risk of a disease of the person based on at least the change amount of the second factor.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority from the Japanese Patent Application No. 2021-110860, filed Jul. 2, 2021, the entire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a health support apparatus, a health support method, and a recording medium recording a health support program.

BACKGROUND

Precision medicine has recently been proposed. The precision medicine is a medicine that analyzes medical treatment methods at the individual level and selects an optimal medical treatment method from the analyzed medical treatment methods. In health guidance against lifestyle-related diseases, optimal goal setting at the individual level is desired as is the case with precision medicine. For example, in order to reduce the diabetic risk, health guidance is desired to be provided in accordance with individual health conditions such that a given person is instructed to achieve 7% weight loss and another person is instructed to achieve 3% weight loss instead of uniformly instructing all persons to achieve 5% weight loss.

DETAILED DESCRIPTION

In general, according to one embodiment, a health support apparatus includes a processor including hardware. The processor accepts input of a first factor that is directly controlled by improving a lifestyle habit of a person subjected to a medical checkup and a second factor that is not directly controlled by improving a lifestyle habit of the person. The processor generates a lifestyle habit combination pattern. The processor calculates change amounts of the first factor and the second factor corresponding to the combination pattern. The processor predicts a disease risk value representing a disease risk of a disease of the person based on at least the change amount of the second factor.

Embodiments will be described below with reference to the accompanying drawings.

First Embodiment

FIG.1is a block diagram showing the arrangement of an example of a health support, apparatus according to the first embodiment. A health support apparatus1includes an input unit100, a search pattern generation unit120, a factor change calculation unit130, a risk prediction unit140, a loss calculation unit150, and a select unit160.

The input unit100accepts the input of medical checkup data and a target value. For example, the input unit100may be configured to accept the input of medical checkup data and a target value by the operation of the health support apparatus1by the user. In this case, the “user” is a person who uses the health support apparatus1. The user may be a person subjected to a medical checkup or a healthcare professional such as a doctor. In addition, the input unit100may be configured to accept medical checkup data stored in a storage medium outside the health support apparatus1(not shown) via a communication medium. Furthermore, the input unit100may be configured to accept medical checkup data transferred from the storage medium in response to when the storage medium is mounted in the health support apparatus1.

In this case, medical checkup data according to this embodiment includes the inspection value of a factor used for the prediction of the risk of a lifestyle-related disease. Such factors include first factors and second factors. The first factors are factors that can be directly controlled by improving the lifestyle habit of a person subjected to a medical checkup. For example, the weight of the person is directly changed by executing exercise and improving the diet. Accordingly, the weight is included in the first factors. In addition, the body fat percentage and the like are included in the first factors. In contrast, the second factors are factors that cannot be directly controlled by improving the lifestyle habit of the person subjected to the medical checkup. For example, HbA1c (hemoglobin A1c) is not directly changed by executing exercise and improving the diet. Accordingly, HbA1c is included in the second factors. In addition, various biopsy values such as GOT (Glutamic Oxaloacetic Transaminase), LDL (Low Density Lipoprotein), and a blood pressure are included in the second factors. Note that HbA1c and the like are factors that cannot be directly controlled by improving the lifestyle habit but can be indirectly changed by a change in weight or the like accompanying the improvement of the lifestyle habit. That is, the second factors include factors that are indirectly controlled by changes in the first factors.

In addition, a target value indicates, for example, to which degree the disease risk value of a specific disease such as a lifestyle-related disease is to be reduced. A target value may be designated by a relative value or absolute value. In this case, the “disease risk value” is the onset probability of a specific disease in a given period. For example, when the disease risk of diabetes is to be reduced from 30% to 20%, the risk reduction target value (relative value) is 10(%) when being designated by a relative value, whereas the risk reduction target value (absolute value) is 20(%) when being designated by an absolute value. Alternatively, a target value may be the one for a change in any of factors such as weight included in medical checkup data. A target value fora change in factor may be designated by an absolute value or relative value like a disease risk value reduction target value.

The search pattern generation unit120generates a lifestyle habit improvement search pattern. A lifestyle habit improvement search pattern includes a plurality of lifestyle habit combination patterns.FIG.2shows an example of lifestyle habit improvement combination patterns. The example shown inFIG.2indicates at least three items, namely “exercise habit”, “daily walking”, and “alcohol drinking” as lifestyle habits. The search pattern generation unit120generates a lifestyle habit improvement combination pattern by, for example, setting “yes” and “no” of these lifestyle habits. For example, setting “yes” for “exercise habit” and “daily walking” indicates that the lifestyle habits will be improved. On the other hand, setting “no” for “alcohol drinking” indicates that the lifestyle habit will be improved. For example, in pattern “No. 1”, “exercise habit”, “daily walking”, and “alcohol drinking” each are set to “no”. Pattern “No. 1” having such setting indicates that avoiding “alcohol drinking” will improve the lifestyle habits. In addition, in pattern “No. 2”, “exercise habit” and “daily walking” are set to “no”, and “alcohol drinking” is set to “yes”. Pattern “No. 2” having such setting indicates that the lifestyle habits will not be improved concerning “exercise habit”, “daily walking”, and “alcohol drinking”. In this case, “no” inFIG.2does not indicate that “exercise habit”, “daily walking”, and “alcohol drinking” are not practiced at all. Note that “no” inFIG.2may include practicing “exercise habit”, “daily walking”, and “alcohol drinking” to such a degree that no influence is imposed on the prediction of a disease risk value. In addition, referring toFIG.2, setting “yes” and “no” concerning the lifestyle habits will generate a lifestyle habit improvement pattern. In contrast to this, a lifestyle habit combination pattern may be generated in additional consideration of the amounts of the respective lifestyle habits. For example, “yes” for exercise habit may be divided into three patterns, namely, “little”, “intermediate”, and “much”.

In this case, the lifestyle habit data used for the generation of a lifestyle habit search pattern by the search pattern generation unit120is stored in, for example, the search pattern generation unit120. Alternatively, the lifestyle habit data may be stored in a storage medium outside the health support apparatus1. In this case, the search pattern generation unit120acquires lifestyle habit data from the external storage medium as needed.

The factor change calculation unit130calculates the change amount of each factor included in the medical checkup data based on the lifestyle habit search pattern generated by the search pattern generation unit120. The factor change calculation unit130predicts the change amount of factor based on, for example, a factor change amount prediction model. This factor change prediction model is, for example, a machine learning model configured to receive medical checkup data and a search pattern and output the change amount of each factor.

An example of a learning method for a factor change prediction model will be described.FIG.3Ais a conceptual diagram of machine learning of the factor change prediction model as the first example. As shown inFIG.3A, machine learning of the factor change prediction model is executed when learning data210is input to a machine learning unit220. Executing learning using a predetermined amount of learning data210will generate a learned model230as a factor change prediction model.

The learning data210includes feature amount data and teacher data. The feature amount data is the data of feature amounts that can be used for the prediction of a change in factor, more specifically, feature amounts such as gender, age, weight, biopsy value, and lifestyle habit. Biopsy values include various types of biopsy values including first and second factors such as blood pressure, GOT, and LDL. Lifestyle habits include Yes or No or amounts of “exercise habit”, “daily walking”, and “alcohol drinking” described above. Teacher data is the actual change amount of a prediction target factor based on a related feature amount.

Using the learning data210, the machine learning unit220learns the relationship between a lifestyle habit combination pattern and the change amount of a factor which changes in accordance with the pattern. The machine learning algorithm in the machine learning unit220may be an arbitrary machine learning algorithm such as neural network, decision tree, or linear regression, which allows learning of the relationship between a feature amount and the change amount of a factor. The machine learning unit220makes the factor change calculation unit130store the learned model230generated as a result of a predetermined amount of learning as a factor change prediction model. The machine learning unit220can be implemented by a computer including a processor and a memory. For example, Kazuyo Tsushita, “Practice and Evaluation of Lifestyle Intervention on Program”, The Journal of the Japanese Society of Internal Medicine/Volume 105/Issue 9, [Online] [Search: Apr. 28, 2021], Internet URL: https://www.jstage.jst.go.jp/article/naika/105/9/105_1654/article/-char/ja/reports that as the weight changes accompanying an improvement in lifestyle habit, a biopsy value such as GOT, HbA1c, or LDL as a second factor significantly changes. That is, improving the lifestyle habit will change a first factor such as weight and biopsy values as second factors such as GOT, HbA1c, and LDL at given ratios. Accordingly, inputting a lifestyle habit search pattern and the values of the respective factors before an improvement in lifestyle habit to this factor change prediction model will make the factor change prediction model output a disease the values of the respective factors that change accompanying the improvement in lifestyle habit. When, for example, the machine learning algorithm is a neural network, the parameters of the neural network, such as a weight coefficient and a bias, are trained such that inputting a lifestyle habit combination pattern and the values of the respective factors before an improvement in lifestyle habit to the neural network will make it output the values of the respective factors that change accompanying the improvement in lifestyle habit. More specifically, the parameters are trained so as to minimize the error between the predicted change amount obtained by the forward propagation of the value of each factor included in the feature amount data to the neural network and the actual change amount included in the teacher data. At this time, the parameters may be trained by inputting a lifestyle habit combination pattern, together with feature amount data, to the neural network or the parameters of the neural network may be trained for each lifestyle habit combination pattern.

A factor change prediction model can be generated for each group such as each gender group or each age group. In this case, learning is executed by using learning data collected for each group.FIG.3Bis a conceptual diagram showing the machine learning of a factor change prediction model as the second example.

Referring toFIG.3B, stratified learning data are represented by N (N is a natural number) learning data, namely, learning data211, learning data212, . . . , learning data21N. The learning data211, the learning data212, . . . , the learning data21N each include feature amount data similar to that of the learning data210. Note, however, that the learning data211, the learning data212, . . . , the learning data21N are stratified according to a feature amount as a stratification target. When, for example, the learning data211, the learning data212, . . . , the learning data21N are stratified according to gender, each data group has commonality in terms of gender. For example, the learning data211can be a male learning data group for male as gender. The learning data212can be a female learning data group as female as gender. Likewise, when the learning data211, the learning data212, . . . , the learning data21N are stratified according to age groups, each data group is learning data based on ages included in the same age group. For example, the learning data211can be learning data in 40's as an age group. The learning data212can be learning data in 60's as an age group.

The machine learning unit220executes learning by individually using each of the learning data211,212, . . . ,21N. The machine learning unit220makes the factor change calculation unit130store learned models231,232, . . . ,23N generated as a result of a predetermined amount of learning as factor change prediction models. Each learned model is obtained by learning using stratified learning data. Accordingly, each learning data allows more learning of the change amount of a factor on the corresponding group. The factor change calculation unit130predicts the change amount of the factor by selecting a learned model for a group corresponding to the input medical checkup data from the learned models231,232, . . . ,23N.

FIG.3Cis a conceptual diagram showing the machine learning of a factor change prediction model as the third example. The third example is an example of stratification with one learned model.

Referring toFIG.3C, the stratified learning data are represented by the learning data211, the learning data212, . . . , the learning data21N. The learning data211, the learning data212, . . . , the learning data21N are learning data stratified in the same manner as the second example. Note, however, that the third example has identifiers added to feature amount data to allow the discrimination of stratified and collected learning data. The identifiers may be identification numbers or the like. For example, the identification number1is added to the feature amount data of the learning data211, and the identification number2is added to the feature amount data of the learning data212.

The machine learning unit220executes learning by using each of the learning data211, the learning data212, . . . , the learning data21N. The machine learning unit220makes the factor change calculation unit130store the learned model230generated as a result of a predetermined amount of learning as a factor change prediction model. The factor change calculation unit130predicts the change amount of the factor by inputting the feature amount data including the same identification number as that of the input medical checkup data to the learned model.

The risk prediction unit140predicts the disease risk value of a disease for each search pattern based on the change amount of the factor predicted by the factor change calculation unit130. The risk prediction unit140predicts, for example, the disease risk value of the disease designated by the user based on, for example, a disease risk prediction model. This risk prediction model is a machine learning model configured to receive, for example, medical checkup data and output a disease risk value for each disease. As a disease risk prediction method, a generally known arbitrary prediction method can be used. As described above, as a lifestyle habit is improved, a first factor such as weight and biopsy values as second factors such as GOT, HbA1c, and LDL change at given ratios. Accordingly, inputting the values of first and second factors after changes accompanying an improvement in lifestyle habit to this risk prediction model will make the risk prediction model output a disease risk value that changes accompanying the improvement in lifestyle habit.

The loss calculation unit150calculates a third loss Loss3 used for selection by the select unit160according to equation (1):

where Loss1 is a first loss based on the difference between a disease risk value predicted by the risk prediction unit140and a target value corresponding to the disease risk value, Loss2 is a second loss caused by changing a lifestyle habit used for prediction and each corresponding factor, that is, an improvement in lifestyle habit and a factor accompanying the improvement, and a is a parameter for adjusting the weights of the first Loss1 and the second loss Loss2.

The loss calculation unit150calculates the first loss Loss1 according to equations (2) given below:

a) If predicted disease risk value risk reduction target value, then

b) If predicted disease risk value>risk reduction target value, then

In addition, the loss calculation unit150calculates the second loss Loss2 according to equation (3) given below:

where Xi is a candidate for the target value for the ith (i is a natural number) input value of a prediction model. In addition, Xi_org is an actual inspection value of the ith input value of the prediction model. Furthermore, Num_X is the number of input values. Note that the respective input values can greatly differ in scale. In this case, the value of Loss2 tends to be influenced by an input value with a large scale. In order to suppress the influence of such a specific input value, the values of Xi and Xi_org may be standardized within the range from 0 to 1.

When the input unit100accepts the target value of a change in a factor included in the medical checkup data, the loss calculation unit150may calculate Loss1 according to equation (4) given below:

where F1 is the change ratio of a factor required to achieve the input factor change target value and F2 is the change ratio of a factor that can be achieved by improving a lifestyle habit. If, for example, an input factor change target value is a target value for GOT, F1 can be the change ratio of the weight required to achieve the GOT change target value and F2 can be the change ratio of the weight that can be achieved by improving a lifestyle habit.

The select unit160selects one or more lifestyle habit combination patterns by using the loss calculated by the loss calculation unit150. For example, when a loss is calculated according to equation (1), a smaller value of the third loss Loss3 indicates that the disease risk value improved by the corresponding lifestyle habit combination pattern is close to the risk reduction target value or the biopsy value of the factor improved by the lifestyle habit combination pattern is closed to the target value. Therefore, for example, when a loss is calculated according to equation (1), the select unit160selects one or more lifestyle habit combination patterns from generated search patterns in ascending order of the value of the third loss Loss3. Depending on the calculation of losses, a larger loss value may indicate that the disease risk value improved by the corresponding lifestyle habit combination pattern is closer to the risk reduction target value or the biopsy value of the factor improved by the lifestyle habit combination pattern is close to the target value. In such a case, the select unit160selects one or more lifestyle habit combination patterns from generated search patterns in descending order of loss value.

FIG.4shows an example of the hardware arrangement of the health support apparatus1. The health support apparatus1includes, as hardware, for example, a processor301, a memory302, an input device303, a display304, a communication device305, and a storage306. The processor301, the memory302, the input device303, the display304, the communication device305, and the storage306are connected to a bus307. The health support apparatus1may be a terminal apparatus such as a personal computer (PC), smartphone, or tablet terminal.

The processor301is a processor that controls the comprehensive operation of the health support apparatus1. The processor301operates as the input unit100, the search pattern generation unit120, the factor change calculation unit130, the risk prediction unit140, the loss calculation unit150, and the select unit160by executing the health support program stored in, for example, the storage306. The processor301is, for example, a CPU. The processor301may be an MPU, GPU, ASIC, FPGA, or the like. The processor301may be a single CPU or the like or a plurality of CPUs or the like.

The memory302includes a ROM and a RAM. The ROM is a nonvolatile memory. The ROM stores a boot program and the like for the health support apparatus1. The RAM is a volatile memory. The RAM is used as a work memory when, for example, the processor301perform processing.

The input device303is an input device including a touch panel, a keyboard, and a mouse. When the input device303is operated, a signal corresponding to the operation is input to the processor301via the bus307. The processor301performs various types of processing in accordance with such signals. The input device303can be used to input, for example, medical checkup data and a risk reduction target value.

The display304is a display such as a liquid display or organic EL display. The display304displays various types of images.

The communication device305is a communication device for allowing the health support apparatus1to communicate with an external device. The communication device305may be a communication device for wired communication or a communication device for wireless communication.

The storage306is a storage such as a flash memory, hard disk drive, or solid state drive. The storage306stores various types of programs executed by the processor301, such as a health support program3061. The storage306also stores lifestyle habit data3062for generating a lifestyle habit search pattern. The lifestyle habit data3062is, for example, an ID assigned to each lifestyle habit. In addition, the storage306stores a factor change prediction model3063. The storage306also stores a risk prediction model3064used for the prediction of a disease risk value. The lifestyle habit data3062, the factor change prediction model3063, and the risk prediction model3064need not always be stored in the storage306. For example, the lifestyle habit data3062, the factor change prediction model3063, and the risk prediction model3064may be stored in a server outside the health support apparatus1. In this case, the health support apparatus1acquires necessary information by accessing the server by using the communication device305.

The bus307is a data transfer path for exchanging data among the processor301, the memory302, the input device303, the display304, the communication device305, and the storage306.

The operation of the health support apparatus1according to the first embodiment will be described next.FIG.5is a flowchart showing the operation of the health support apparatus1according to the first embodiment. The processing inFIG.5is executed by the processor301.

In step S1, the processor301acquires medical checkup data and a target value. The medical checkup data may be input via the operation of the input device303by the user on the GUI (Graphical User Interface) displayed on the display304or input via a storage medium outside the health support apparatus1. The target value may be input via the operation of the input device303by the user on the GUI displayed on the display304.

In step S2, the processor301generates a lifestyle habit search pattern by referring to the lifestyle habit data3062. The processor301may generate a search pattern based on all lifestyle habit combinations generated from the lifestyle habit data3062or generate a search pattern based on some lifestyle habit combinations.

In step S3, the processor301calculates the change amount of a factor used for the prediction of a disease risk for each search pattern. For example, the processor301predicts the change amount of a factor by inputting a search pattern and medical checkup data to the factor change prediction model.

In step S4, the processor301predicts a disease risk value. For example, the processor301predicts a disease risk value for each search pattern by inputting the value of the factor after the change to a disease risk prediction model.

In step S5, the processor301executes loss calculation. For example, the processor301calculates losses according to equations (1), (2), and (3) or equations (1), (2), and (4).

In step S6, the processor301selects a lifestyle habit combination pattern to be presented to the user. For example, the processor301selects one or more lifestyle habit combination patterns in ascending order of the values of losses.

In step S7, the processor301presents the result to the user. Thereafter, the processor301terminates the processing inFIG.5. For example, the processor301displays the selected lifestyle habit combination pattern and a disease risk value predicted from the corresponding lifestyle habits on the screen of the display304. A reduction value of the disease risk value or the reduction target value of a factor such as weight and the value of a factor improved by a lifestyle habit may be displayed instead of a disease risk value.

As described above, according to the first embodiment, a machine learning model is used to predict the change amounts of the first factor that can be directly controlled by improving the lifestyle habit of a person subjected to a medical checkup in accordance with a lifestyle habit combination pattern of the person subjected to the medical checkup and the second factor that cannot be directly controlled by improving the lifestyle habit of the person subjected to the medical checkup. A disease risk value can be predicted based on the first and second factors. For example, the weight is reduced over a long period of time, such as after half a year or one year, by improving a lifestyle habit, and the biopsy value is improved by a long-term reduction in weight. According to the first embodiment, a disease risk value can be calculated in consideration of such a long-term reduction in weight and a long-term change in biopsy value. In this manner, in the first embodiment, the second factor that cannot be directly controlled by improving the lifestyle habit of a person subjected to a medical checkup is properly reflected in a prediction result in the prediction of a disease risk value, and hence optimal goal setting at the individual level can be implemented in health guidance.

Stratifying learning data used for the prediction of changes in factors makes it possible to predict the change amounts of the first and second factors of a person subjected to a medical checkup at the individual level. This can provide the person subjected to a medical checkup with more appropriate guidance.

Second Embodiment

The second embodiment will be described next. A factor change calculation unit130according to the second embodiment calculates the change amount of each factor included in medical checkup data based on the lifestyle habit search pattern generated by a search pattern generation unit120.FIG.6shows the arrangement of an example of the factor change calculation unit130according to the second embodiment. As shown inFIG.6, the factor change calculation unit130may include a first factor change calculation unit131and a second factor change calculation unit132.

The first factor change calculation unit131calculates the change amount of the first factor based on the lifestyle habit search pattern. The second factor change calculation unit132calculates the change amount of the second factor. When a change in the first factor influences a change in the second factor, the second factor change calculation unit132calculates the change amount of the second factor based on the change amount of the first factor.

FIGS.7A and7Bare views each showing an example of a weight change ratio table as an example of a first factor change table. A weight change ratio is a value representing a weight change by percentage from the original weight. A weight change ratio table is a table representing a weight change ratio accompanying a lifestyle habit improvement. In the second embodiment, a weight change ratio table is prepared for each group.FIG.7Ashows an example of a 40's male weight change ratio table.FIG.7Bshows an example of a 60's male weight change ratio table. A weight change ratio table can be generated by actually measuring changes in weight accompanying lifestyle habit improvements with respect to many persons in age groups and gender groups. Obviously, weight change ratio tables in other age groups and weight change ratio tables for females may be further prepared.

Referring toFIGS.7A and7B, weight change ratios are divided into three groups, namely, a metabolic syndrome group, a potential metabolic syndrome group, and a non-metabolic syndrome group. The first factor change calculation unit131calculates a weight change ratio concerning a first factor by using the weight change ratio table and calculates the change amount of weight based on the calculated weight change ratio. The change amount of weight is, for example, the product of the weight of a person subjected to a medical checkup and a weight change ratio. In this case, the weight change ratio table may include weight change ratios corresponding to the amounts of lifestyle habits.

The tables inFIGS.7A and7Bcan be used as tables for calculating weight change ratios. Tables similar to those shown inFIGS.7A and7Bare prepared for the respective types of first factors. The first factor change calculation unit131calculates the change amount of each first factor by using a first factor change table for a corresponding group.

FIGS.8A and8Beach are an example of a second factor change table.FIGS.8A and8Beach show a table representing the relationship between weight change ratios and second factor change ratios. In the second embodiment, second factor change tables are prepared for the respective groups.FIG.8Ashows an example of a second factor change table for 40's males.FIG.8Bshows an example of a second factor change table for 60's males. A second factor change table can be generated by actually measuring changes in biopsy value accompanying weight changes with respect to, for example, many persons in age groups and gender groups. Obviously, second factor change tables in other age groups and second factor change tables for age groups of females may be prepared.

The second factor change calculation unit132calculates the change amounts of second factors based on the change amount of a first factor for each lifestyle habit search pattern calculated by the first factor change calculation unit131. For example, referring toFIG.8A, when the weight change amount is from −1% to +1%, the change ratios of GOT, HbA1c, and LDL each are 0.00%. In contrast to this, when the weight change amount is from −1% to −3%, the change ratios of GOT, HbA1c, and LDL are respectively −2.00%, −1.00%, and −3.00%.

The tables inFIGS.8A and8Bcan be used as tables for the calculation of the change amounts of the second factors from weight change ratios. Tables similar to those shown inFIGS.8A and8Bare prepared for the respective types of first factors. The second factor change calculation unit132calculates the change amount of each second factor by using a second factor change table for a corresponding group.

In addition, the factor change calculation unit130calculates the change amount of a factor in accordance with the lifestyle habit search pattern generated by the search pattern generation unit120. The factor change calculation unit130may totalize change ratios for the respective individual improvement patterns of lifestyle habits such as exercise and daily walking or may calculate a change amount by combining a plurality of lifestyle habits.

As the hardware arrangement of the health support apparatus1, the arrangement shown inFIG.4can be basically used. Note, however, that a storage306can store first factor change tables and second factor change tables. The first factor change tables and the second factor change tables need not always be stored in the storage306.

The operation of the health support apparatus1according to the second embodiment will be described next.FIG.9is a flowchart showing the operation of the health support apparatus1according to the second embodiment. The processing inFIG.9is executed by a processor301.

In step S11, the processor301acquires medical checkup data and a target value. The medical checkup data may be input, for example, via the operation of an input device303by the user on the GUI displayed on a display304or may be input via a storage medium outside the health support apparatus1. The target value may be input via the operation of the input device303by the user on the GUI displayed on the display304.

In step S12, the processor301generates a lifestyle habit search pattern by referring to lifestyle habit data3062. The processor301may generate a search pattern by combining all or some of the lifestyle habits that can be generated from the lifestyle habit data3062.

In step S13, the processor301calculates the change amount of a factor used for the prediction of a disease risk for each search pattern. When a change in first factor influences a change in second factor, the processor301calculates the change amount of the first factor first by using a first factor change table and then calculates the change amount of the second factor by using a second factor change table.

In step S14, the processor301predicts a disease risk value. For example, the processor301inputs the value of the factor after the change to a disease risk prediction model and predicts a disease risk value for each search pattern.

In step S15, the processor301executes loss calculation. For example, the processor301calculates losses according to equations (1), (2), and (3) or (1), (2), and (4).

In step S16, the processor301selects a lifestyle habit combination pattern presented to the user. For example, the processor301selects one or more lifestyle habit combination patterns in ascending order of the values of losses.

In step S17, the processor301presents the result to the user. The processor301then terminates the processing inFIG.9. For example, the processor301displays the selected lifestyle habit combination pattern and the disease risk value predicted in the lifestyle habits on the screen of the display304. A reduction value of the disease risk value may be displayed instead of a disease risk value or the reduction target value of a factor such as weight and the biopsy value of the factor improved by a lifestyle habit may be displayed.

As described above, the second embodiment is configured to calculate, based on tables, the change amounts of the first factor that can be directly controlled by improving the lifestyle habit of a person subjected to a medical checkup in accordance with a lifestyle habit combination pattern of the person subjected to the medical checkup and the second factor that cannot be directly controlled by improving the lifestyle habit of the person subjected to the medical checkup. According to the second embodiment, a disease risk value can be predicted based on the first and second factors. In this manner, as in the first embodiment, in the second embodiment, the second factor that cannot be directly controlled by improving the lifestyle habit of a person subjected to a medical checkup is properly reflected in a prediction result in the prediction of a disease risk value, and hence optimal goal setting at the individual level can be implemented in health guidance.

The tables used for the calculation of changes in factor are prepared for the respective groups such as gender groups and age groups. Accordingly, the change amounts of factors can be calculated in accordance with lifestyle habit combination patterns including differences in gender and age among persons subjected to medical checkups. This makes it possible to use the change amounts of factors for the respective groups based on differences in gender, age, and the like for risk prediction in the second embodiment as in the first embodiment, thereby providing a person subjected to a medical checkup with proper guidance.

The first and second embodiments each have exemplified the stratification based on differences in gender and the stratification based on differences in age. However, stratification may be performed in a different manner. For example, stratification may be performed depending on whether a certain drug is administered. In addition, stratification may be performed based on BMI (Body Mass Index) values. Furthermore, stratification may be performed based on dietary habits such as easing a lot of meats, not eating a lot of meats, or vegetarianism. Moreover, stratification may be performed in an arbitrary manner such as stratification based on whether there is any obese person in a family. These stratifications may be singly used or in combination.