CN114496236A - Health condition evaluation method and health condition evaluation device - Google Patents

Health condition evaluation method and health condition evaluation device Download PDF

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CN114496236A
CN114496236A CN202011267470.6A CN202011267470A CN114496236A CN 114496236 A CN114496236 A CN 114496236A CN 202011267470 A CN202011267470 A CN 202011267470A CN 114496236 A CN114496236 A CN 114496236A
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health condition
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殷颖
松森正树
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Hitachi Ltd
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    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
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Abstract

The present invention relates to a health condition evaluation device, including: a storage unit that stores index data of a plurality of indexes relating to the health of a plurality of users and evaluation data of the health condition of the user; an index dimension reduction unit that performs dimension reduction processing on the plurality of indexes by a statistical method based on the data in the storage unit to obtain a plurality of comprehensive variables that determine the health condition and are represented by effective indexes among the plurality of indexes; a model construction unit that constructs a preliminary mathematical model between the synthetic variables and the health condition by using a neural network algorithm, and converts the preliminary mathematical model into a health condition evaluation model constructed between the effective index and the health condition; an index data acquisition unit that acquires index data corresponding to an effective index for a subject; and an evaluation unit that obtains evaluation data of the health condition of the subject by substituting the index data acquired by the index data acquisition unit into the health condition evaluation model.

Description

Health condition evaluation method and health condition evaluation device
Technical Field
The present invention relates to a health condition evaluation method and a health condition evaluation device for evaluating the health condition of a subject.
Background
In recent years, the problem of aging has been increasing, and the health condition of the aged has been receiving more attention. The development of technologies such as artificial intelligence and the like brings great changes to the health care industry, and the problem of how to prolong the health life by using technical means is a subject.
Patent document 1 discloses an intelligent platform monitoring system for elderly people, which is composed of an intelligent bracelet with a smoke sensor for preventing gas leakage and fire, a pulse measurement function, a sleep monitoring function, a fall recognition function, a GPS function, and an SOS function, and a data collection and processing module, and aims to protect the daily safety of the elderly people.
Documents of the prior art
Patent document
Patent document 1: CN110060455A
Disclosure of Invention
Technical problem to be solved by the invention
However, although the system such as patent document 1 can protect the user easily, it cannot grasp the health condition of the user accurately. Further, the health condition of the user cannot be dynamically analyzed using daily data.
Technical means for solving the technical problem
The present invention has been made to solve the above-mentioned problems, and provides a health condition evaluation device including: a storage unit that stores historical health data of a plurality of users, the historical health data including index data of a plurality of indexes relating to health of the user and evaluation data of health conditions of the user; an index dimension reduction unit that performs dimension reduction processing on the plurality of indexes by a statistical method based on the historical health data stored in the storage unit, thereby obtaining a plurality of comprehensive variables that determine the health condition and are represented by effective indexes among the plurality of indexes; a model construction unit that constructs a preliminary mathematical model between the synthetic variables and the health condition using a neural network algorithm, and converts the preliminary mathematical model into a health condition evaluation model constructed between the effective indicators and the health condition based on a relationship between the plurality of synthetic variables and the effective indicators; an index data acquisition unit that acquires index data corresponding to the effective index for a subject; and an evaluation unit that obtains evaluation data of the health condition of the subject by substituting the index data collected by the index data collection unit into the health condition evaluation model.
Thus, the health condition of the subject can be accurately grasped, and the health condition of the subject can be dynamically analyzed using the daily data. And the operation amount can be reduced and the operation speed can be improved by reducing the dimension of the index.
In the above-described health condition evaluation device, it is preferable that the health condition evaluation device further includes an output unit that outputs at least one of index data corresponding to the effective index obtained by the index dimension reduction unit and evaluation data of the health condition of the subject obtained by the evaluation unit.
Therefore, the evaluation result can be output to the user, and the problem reflected by the data model is more intuitive.
In the health condition evaluation device, the statistical method may be a principal component analysis method or another statistical method.
In the health condition evaluation device, the index may be at least one of a test index of a sex, an age, an educational level, an adverse habit, a height, a weight, a pulse, a blood pressure, a heart rate, a body temperature, a weight, a blood sugar, a blood fat, a sleep quality of the user, a diagnosed disease index, a test index of a cognitive ability, a test index of a finger movement ability, and a test index of a gait condition of the user.
In the health condition evaluation device, the evaluation data of the health condition may be any one of an MMSE score, a TMT score, a TUG score, and a sleep score.
The present invention also provides a health condition evaluation method, including: a historical data acquisition step of acquiring historical health data of a plurality of users, wherein the historical health data comprises index data of a plurality of indexes related to the health of the users and evaluation data of the health conditions of the users; an index dimension reduction step, wherein dimension reduction processing is carried out on the plurality of indexes by adopting a statistical method according to the historical health data obtained in the historical data obtaining step, so that a plurality of comprehensive variables which determine the health condition and are represented by effective indexes in the plurality of indexes are obtained; a model construction step, adopting a neural network algorithm to construct a preliminary mathematical model between the comprehensive variables and the health condition, and converting the preliminary mathematical model into a health condition evaluation model constructed between the effective indexes and the health condition according to the relationship between the comprehensive variables and the effective indexes; an index data acquisition step of acquiring index data corresponding to the effective index for the subject; and an evaluation step of substituting the index data acquired in the index data acquisition step into the health condition evaluation model, thereby obtaining evaluation data of the health condition of the subject.
Thus, the health condition of the subject can be accurately grasped, and the health condition of the subject can be dynamically analyzed using the daily data. And the operation amount can be reduced and the operation speed can be improved by reducing the dimension of the index.
In the above-described health condition evaluation method, it is preferable that the health condition evaluation method further includes an output step of outputting at least one of index data corresponding to the effective index obtained in the index dimension reduction step and evaluation data of the health condition of the subject obtained in the evaluation step.
Therefore, the evaluation result can be output to the user, and the problem reflected by the data model is more intuitive.
In the above-described health condition evaluation method, the statistical method may be a principal component analysis method or another statistical method.
In the health condition evaluation method, the index may be at least one of a test index of gender, age, education level, bad habit, height, weight, pulse, blood pressure, heart rate, body temperature, weight, blood sugar, blood fat, sleep quality, a diagnosed disease index, a test index of cognitive ability, a test index of finger activity ability, and a test index of gait condition of the user.
In the health condition evaluation method, the evaluation data of the health condition may be any one of an MMSE score, a TMT score, a TUG score, and a sleep score.
Drawings
Fig. 1 is a block diagram of a health condition evaluation device 100 according to a first embodiment of the present invention.
Fig. 2 shows an example of the output evaluation data of the health condition of the subject.
Fig. 3 shows another example of the output evaluation data of the health condition of the subject.
Fig. 4 is a flowchart of a health condition evaluation method according to a second embodiment of the present invention.
Detailed Description
[ first embodiment ]
Fig. 1 is a configuration diagram of the health condition evaluation device 100.
The health condition evaluation device 100 includes a storage unit 110, a calculation unit 120, an input unit 130, and an output unit 140. The calculation unit 120 includes an index dimension reduction unit 121, a model construction unit 122, and an evaluation unit 123.
The storage unit 110 stores historical health data of a plurality of users, the historical health data including index data of a plurality of indexes related to the health of the user and evaluation data of the health condition of the user.
Such an index may be basic information such as sex, age, education level, and bad habits, a daily index that can be easily obtained such as height, weight, pulse, blood pressure, heart rate, body temperature, weight, blood sugar, and blood fat, an index of a disease that has been diagnosed such as hypertension, cardiovascular disease, and diabetes, or an index of a test performed by a medical worker or other professionals for sleep quality, cognitive ability, finger movement ability, and gait condition according to a test method that is internationally common.
For example, as the test index of the sleep quality, there may be a total sleep time, a deep sleep time, a light sleep time, a number of times of getting up to night, a time of getting up to night, and the like. The test index of the finger movement ability may be a total movement distance of the finger opening and closing movement, a maximum amplitude of the speed and the acceleration, a standard deviation, a variation coefficient, the number of opening and closing times, an average of the opening and closing intervals, a standard deviation, a variation coefficient, a phase difference between both hands, or the like within a predetermined time. The gait condition test indexes include average single step time, average step length, average stride, average step width, average foot included angle, pace speed, step frequency and the like.
The index data of the indexes can be obtained from various channels, such as uploaded data from terminal devices used by an elderly care institution, a medical institution or an individual user, and the terminal devices are used for monitoring body functions, such as body data measuring instruments like a brain function imager, a finger activity detector and a gait analyzer, intelligent data sensors like an infrared sensor, a temperature and humidity sensor, a smoke sensor and a motion sensor, intelligent home equipment like an intelligent mattress and an intelligent bracelet, and the like.
The health condition of the user may be any one of sleep quality, cognitive ability, finger activity ability, and gait condition, and the evaluation data on the health condition may be, as the data of the sleep quality, for example, a sleep score obtained according to various known test methods may be used, as the data of the cognitive ability, for example, an MMSE (simple mental state examination table) score may be used, as the data of the finger activity ability, for example, a TMT (inline test) score may be used, and as the data of the gait condition, for example, a TUG (rise-and-walk timing test) score may be used.
In the present embodiment, considering that the number of indicators in the historical health data is large, if a mathematical model is directly constructed between these indicators and the health condition of the user, the amount of calculation and the calculation time are enormous. In addition, some indexes may have correlation, so that the influence on the health condition of the user may have overlapping parts, and the calculation result of the constructed mathematical model is inaccurate.
In view of the above, in the present embodiment, the index is subjected to dimension reduction processing before the mathematical model is constructed. Specifically, the index dimension reduction unit 121 obtains a plurality of integrated variables that determine the health condition as the index after dimension reduction by performing dimension reduction processing on the plurality of indexes by a statistical method such as Principal Component Analysis (PCA) based on the historical health data stored in the storage unit 110. The comprehensive variables do not have correlation with each other and independently affect the health condition, each comprehensive variable can be characterized by a part of indexes in the indexes, and the part of indexes used for characterizing the comprehensive variables are indexes which have actual influence on the health condition and are called effective indexes. The index dimension reduction unit 121 stores the obtained effective index in the storage unit 110. The "statistical method" is not limited to the principal component analysis method as long as the above effects can be achieved, and may be a nonlinear principal component analysis method, for example.
In this way, by reducing the dimension, an unnecessary index that does not actually affect the health status, other than the effective index, can be eliminated from the plurality of indexes, and unnecessary and repeated calculation by the model building unit 122, which will be described later, can be avoided, so that the amount of calculation is reduced, and the calculation speed is increased.
After the dimension reduction, the model construction section 122 constructs a preliminary mathematical model between the above-obtained comprehensive variables and the health condition using a neural network algorithm. The neural network algorithm may be any one of a linear regression method and a support vector regression method, and is not limited herein.
After obtaining the preliminary mathematical model, the model construction unit 122 converts the preliminary mathematical model into a health condition evaluation model, which is a final model constructed between the effective index and the health condition, based on the conversion relationship between the plurality of integrated variables and each effective index obtained in the dimension reduction process, and stores the final model in the storage unit 110. The model can be used to evaluate the health condition of a subject.
The input unit 130 serves as an index data acquisition unit for acquiring index data corresponding to the effective index for the subject. The input unit 130 may be, for example, a character input device such as a touch panel or a tablet, an audio input device such as an audio receiver, an image input device such as a camera, or the like.
The evaluation unit 123 substitutes the index data of the effective index acquired from the input unit 130 into the health condition evaluation model, obtains evaluation data of the health condition of the subject through calculation, and stores the evaluation data in the storage unit 110.
The output unit 140 outputs the evaluation data of the health condition of the subject stored in the storage unit 110, and simply indicates which index has an influence on the health condition of the subject by outputting index data corresponding to the effective index and/or the evaluation data of the health condition for different subjects in a list form as shown in fig. 2, for example, on a display unit not shown. In the illustrated example, the health conditions include three items, i.e., "sleep quality", "cognitive ability", and "gait condition", and therefore three corresponding mathematical models need to be constructed for calculating evaluation data of the three health conditions. In constructing these three mathematical models, the evaluation data of "sleep quality", the evaluation data of "cognitive ability", and the evaluation data of "gait condition" in the historical health data are used, respectively.
[ modified example of the first embodiment ]
The specific configuration of the present embodiment is not limited to the above description, and may be in other forms.
For example, the input unit 130 and the display unit may be two separate members or may be an integrated structure such as a touch panel.
The output unit 140 may be a display unit or other output devices such as an audio device.
When the health condition evaluation device 100 is networked, the effective index obtained by the index dimensionality reduction unit 121, the health condition evaluation model obtained by the model construction unit 122, and the evaluation data of the health condition of the subject obtained by the evaluation unit 123 may be stored in corresponding servers. Further, the acquisition of the index data of the effective index of the subject may be performed by registering the index data of the subject with software or the like installed on a mobile terminal (for example, a mobile phone) of the subject without being directly input using the input unit 130, and the index data of the subject may be transmitted to the server through the mobile terminal.
In addition, when the evaluation data for displaying the health condition is output, the calculated evaluation data for the health condition can be compared with the average value of the same age group, so that the display result is more intuitive.
Furthermore, it is considered that some health abnormalities are often associated with emergency situations, for example, when the evaluation data of "gait conditions" is abnormal, there may be a risk of falling, which is particularly important in the case where the subject is an elderly person. Therefore, the health condition evaluation apparatus 100 can be used for tracking and monitoring the health condition of the elderly person, and in this case, the health condition evaluation apparatus 100 includes an alarm unit (not shown) and, when the evaluation data of the "gait condition" is lower than a certain threshold (that is, there is a risk of falling), gives an alarm to the user to avoid the subject from falling. Alternatively, the alarm unit may issue an alarm when index data of a certain index (e.g., heart rate or night time of leaving bed) is lower or higher than a predetermined threshold (e.g., abnormal heart rate or fall at night).
Further, as shown in fig. 3, index data of the effective index and a change in the evaluation data of the health condition in a predetermined time period may be displayed for different subjects, and the change in the health condition of the subject may be dynamically evaluated.
[ second embodiment ]
The present embodiment relates to a health condition evaluation method, and fig. 4 is a flowchart of the health condition evaluation method.
First, in step S401, a historical data acquisition step is performed to acquire historical health data of a plurality of users, the historical health data including index data of a plurality of indexes related to the health of the user and evaluation data of the health condition of the user. The definition of the index and the health condition is as described in the first embodiment, and the description thereof is omitted here.
Next, the process proceeds to step S402, where an index dimension reduction step is performed, and the above-described dimension reduction processing is performed on the plurality of indexes by using a statistical method such as a principal component analysis method based on the historical health data acquired in step S401, thereby obtaining a plurality of comprehensive variables that determine the health condition and are represented by effective indexes among the plurality of indexes. The details of the dimension reduction are as described in the first embodiment, and the description thereof is omitted here.
Next, the process proceeds to step S403, where a model construction step is performed, a preliminary mathematical model is constructed between the synthetic variables obtained in step S402 and the health conditions using a neural network algorithm, and the preliminary mathematical model is converted into a health condition evaluation model constructed between the effective indicators and the health conditions as a final model according to the relationship between the synthetic variables and the effective indicators. The details of the construction of the preliminary mathematical model and the final model are as described in the first embodiment, and the description thereof is omitted here.
Next, the process proceeds to step S404, where an index data collection step is performed to collect index data corresponding to the effective index for the subject. The acquisition mode may be a common multiple acquisition mode as described in the first embodiment.
Next, the process proceeds to step S405, and an evaluation step is performed to substitute the index data acquired in step S404 into the obtained health condition evaluation model, thereby obtaining evaluation data of the health condition of the subject.
Finally, the process proceeds to step S406, and an output step is performed to output at least one of index data corresponding to the effective index obtained in step S402 and evaluation data of the health condition of the subject obtained in step S405. The output mode of the evaluation result may be a visual output or an audible output, as described in the first embodiment.
Although the present invention has been described with reference to certain preferred embodiments thereof, it will be apparent to those skilled in the art that the present invention is not necessarily limited to the embodiments having all the configurations described above, and that the embodiments may be combined with each other or a part of the configuration of one embodiment may be replaced with the configuration of another embodiment, the configuration of another embodiment may be added to the configuration of one embodiment, and addition, deletion, or replacement of another configuration may be performed to a part of the configuration of each embodiment, within a range not departing from the technical spirit of the present invention.

Claims (10)

1. A health condition evaluation device characterized by comprising:
a storage unit that stores historical health data of a plurality of users, the historical health data including index data of a plurality of indexes relating to health of the user and evaluation data of health conditions of the user;
an index dimension reduction unit that performs dimension reduction processing on the plurality of indexes by a statistical method based on the historical health data stored in the storage unit, thereby obtaining a plurality of comprehensive variables that determine the health condition and are represented by effective indexes among the plurality of indexes;
a model construction unit that constructs a preliminary mathematical model between the synthetic variables and the health condition using a neural network algorithm, and converts the preliminary mathematical model into a health condition evaluation model constructed between the effective indicators and the health condition based on a relationship between the synthetic variables and the effective indicators;
an index data acquisition unit that acquires index data corresponding to the effective index for a subject; and
an evaluation unit that obtains evaluation data of the health condition of the subject by substituting the index data collected by the index data collection unit into the health condition evaluation model.
2. The health condition evaluation device according to claim 1,
the health evaluation device further includes an output unit that outputs at least one of index data corresponding to the effective index obtained by the index dimension reduction unit and evaluation data of the health condition of the subject obtained by the evaluation unit.
3. The health condition evaluation device according to claim 1 or 2,
the statistical method is a principal component analysis method.
4. The health condition evaluation device according to claim 1 or 2,
the index includes at least one of sex, age, education level, bad habit, height, weight, pulse, blood pressure, heart rate, body temperature, weight, blood sugar, blood fat, test index of sleep quality, diagnosed disease index, test index of cognitive ability, test index of finger activity ability and test index of gait condition of the user.
5. The health condition evaluation device according to claim 1 or 2,
the evaluation data of the health condition is any one of an MMSE score, a TMT score, a TUG score, and a sleep score.
6. A health condition evaluation method is characterized by comprising:
a historical data acquisition step of acquiring historical health data of a plurality of users, wherein the historical health data comprises index data of a plurality of indexes related to the health of the users and evaluation data of the health conditions of the users;
an index dimension reduction step of performing dimension reduction processing on the plurality of indexes by adopting a statistical method according to the historical health data acquired in the historical data acquisition step so as to acquire a plurality of comprehensive variables which determine the health condition and are characterized by effective indexes in the plurality of indexes;
a model construction step, adopting a neural network algorithm to construct a preliminary mathematical model between the comprehensive variables and the health condition, and converting the preliminary mathematical model into a health condition evaluation model constructed between the effective indexes and the health condition according to the relationship between the comprehensive variables and the effective indexes;
an index data acquisition step of acquiring index data corresponding to the effective index for the subject; and
and an evaluation step of substituting the index data acquired in the index data acquisition step into the health condition evaluation model to thereby obtain evaluation data of the health condition of the subject.
7. The health condition evaluation method according to claim 6,
further comprising an output step of outputting at least one of index data corresponding to the effective index obtained in the index dimension reduction step and evaluation data of the health condition of the subject obtained in the evaluation step.
8. The health condition evaluation method according to claim 6 or 7,
the statistical method is a principal component analysis method.
9. The health condition evaluation method according to claim 6 or 7,
the index includes at least one of sex, age, education level, bad habit, height, weight, pulse, blood pressure, heart rate, body temperature, weight, blood sugar, blood fat, test index of sleep quality, diagnosed disease index, test index of cognitive ability, test index of finger activity ability and test index of gait condition of the user.
10. The health condition evaluation method according to claim 6 or 7,
the evaluation data of the health condition is any one of an MMSE score, a TMT score, a TUG score, and a sleep score.
CN202011267470.6A 2020-11-13 2020-11-13 Health condition evaluation method and health condition evaluation device Pending CN114496236A (en)

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