CN117594228A - Health star-level intelligent evaluation method, system and storage medium - Google Patents
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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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
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Abstract
The invention belongs to the technical field of health management, and particularly relates to a health star-level intelligent evaluation method, a system and a storage medium. The method of the invention comprises the following steps: step 1, collecting and inputting indexes of a subject; and 2, calculating the index by adopting an HE algorithm model to obtain the health gap index of the subject. The HE algorithm model constructed by the invention can eliminate the influence of subjective factors in health evaluation, gives consideration to the more comprehensive and objective results, and has good application prospect.
Description
Technical Field
The invention belongs to the technical field of health management, and particularly relates to a health star-level intelligent evaluation method, a system and a storage medium.
Background
Health assessment is the process of systematically collecting and analyzing patient's health data to ascertain their health status, the health problems present, and their possible causes, and to assist doctors in obtaining follow-up care diagnostic results. Health assessment is the basis and assurance of implementing overall care, a requisite ability for healthcare workers. The key is to collect, analyze and sort the health related data of the patient comprehensively, systematically, accurately and dynamically, determine the existing or potential problems of the patient and facilitate the subsequent diagnosis.
Several health assessment methods that are currently popular are:
(1) BMI (Body Mass Index) is a standard commonly used internationally to measure the degree of obesity and well being of a human. The calculation formula is as follows: BMI = weight/height 2 . (weight unit: kg; height unit: m.).
(2) HRA health risk assessment: by adopting bioelectric induction technology and combining with human body electrical impedance measurement technology and applying a timing current statistical analysis method, 3D reconstruction is carried out on human tissue and organs, the change trend of the whole body viscera can be intuitively seen, early diseases are judged, and thus, the human health condition is evaluated.
(3) The existing questionnaire design and evaluation project mechanism is relatively more, for example, SF-36 scale and physical activity screening are carried out by adopting PAR-Q, diet investigation review, psychological means such as scl-90 and the like. For multidimensional assessment of customer health.
Health is a state of well being in all aspects of physical, mental and social adaptations, as defined by WHO (world health organization). The mere physical absence of disease cannot be judged as healthy. The existing health assessment techniques therefore have mainly the following two drawbacks:
(1) Many special industries are admitted, the requirement on personal physical conditions is very high, a comprehensive physical condition grade assessment method is needed, the existing method is simple comparison of single, sporadic and unilateral actual measurement indexes and standard indexes, only the problem existing on one side of health is reflected by the method, and the obtained assessment result is definitely unilateral and inaccurate. However, since the health index is very large, the method of assessing the health of an individual's population is lacking.
(2) The aggregation of individual indices is difficult to visually compare in a high-dimensional space, so that the index is summarized by using weights determined by manpower and experts generally, thereby giving a comprehensive score, but the method has high randomness and uncertainty.
Based on the above problems, how to consider the comprehensiveness and objectivity of the evaluation result in the health evaluation of individuals is a problem to be solved in the art.
Disclosure of Invention
Based on the problems of the prior art, the invention provides a healthy star-level intelligent evaluation method, a healthy star-level intelligent evaluation system and a healthy evaluation method storage medium, and aims to provide a healthy evaluation method which has comprehensive indexes and does not need to manually determine the indexes and weights thereof, thereby realizing comprehensive and objective personal health evaluation.
A healthy star-level intelligent evaluation method comprises the following steps:
step 1, collecting and inputting indexes of a subject;
step 2, calculating the index by adopting an HE algorithm model to obtain a health gap index of the subject;
the calculation process of the HE algorithm model comprises the following steps:
step a, carrying out pretreatment of negation and normalization on negative indexes in the indexes;
step b, preprocessing section indexes in the indexes as follows:
assuming that when a certain index shows normal conditions are satisfied with x epsilon a, b, the following transformation is performed on the index:
wherein x' i ' is a transformed index, x i Is an index before transformation;
step c, assuming that each health index is calculated according to the optimal weight, calculating an ideal value, wherein the calculation formula is as follows:
wherein maxV j For the ideal value, y ij Is the ith index of the jth individual inspector, w i Is the weight of the ith index, w i+a Is the weight of the (i+a) th index, w r Is the weight of the r index, m is the number of indexes, n is the number of evaluated people, b a Is a constant between 0 and 1 which can be customized, and is used for adding prior knowledge w about weight when a certain weight needs to be customized i -w i+a ≥b a S.t. represents a constraint;
step d, finding a value V from the ideal value * (w) V (w) nearest to the nearest V (w), the calculation formula is:
wherein min is minimum value operation, D 2 As a distance function of 2-range, V (w) is the distance from the ideal point V * (W) the nearest point, W is the set of ownership weights, V j * Is maxV obtained in step c j 。
Preferably, the negative index is inverted by taking the reciprocal thereof and replacing the original index data with the reciprocal.
Preferably, the method for normalizing the negative indicators comprises the following steps:
wherein x' is the index after transformation, x is the index before transformation, mu is the average value of the index, x max Is the maximum value of the index, x min Is the minimum value of the index.
Preferably, when the weight of the index needs to be manually adjusted, the related weight is added in the constraint conditionPrior knowledge w of (2) i -w i+a ≥b a 。
Preferably, the category of the index includes at least one of the following indexes: is used for evaluating indexes of genes, physiology, psychology, sports, nutrition and environment.
Preferably, the index for evaluating physiology includes at least one of the following indexes: hemoglobin, red blood cells, white blood cells, platelet count, fasting blood glucose, serum total cholesterol, serum triacylglycerol, uric acid, troponin I, creatinine, alanine aminotransferase, serum total bilirubin, serum-bound bilirubin, serum-unbound bilirubin, carcinoembryonic antigen, alpha fetoprotein, rheumatoid factor, reactive protein, procalcitonin;
the index for evaluating psychology includes at least one of the following indexes: intellectual, he, anxiety, depression, etc. self-rating;
the index for evaluating nutrition includes at least one of the following indexes: sodium, potassium, magnesium, calcium, phosphorus, zinc, iodine, selenium, manganese;
the index for evaluating exercise includes at least one of the following indexes: body mass index, pulse, total testosterone in early morning resting state, measurement, plasma cortisol, CD4/CD8, igA, igM, igG, blood ammonia, urine specific gravity;
the index for evaluating the environment includes at least one of the following indexes: pesticide pollution, atmospheric pollution and soil pollution.
Preferably, the health gap index is used to rank according to the following threshold values:
five star grade: the index gap index is more than or equal to 90;
four star stages: the index gap index of 90 is more than or equal to 75;
three star stages: 75> index gap index is more than or equal to 60;
two star stages: the index gap index of 60 is more than or equal to 45;
a star grade: 45> index gap index.
The invention also provides a healthy star-level intelligent evaluation system, which comprises:
the input module is used for inputting the index of the subject;
the calculation module is used for calculating according to the healthy star-level intelligent evaluation method;
and the output module is used for outputting the calculation result of the calculation module.
The present invention also provides a computer-readable storage medium having stored thereon: a computer program for implementing the above health assessment method.
In the present invention, the "negative index" refers to an index that reflects the better the health state of the subject as the index value is smaller; the term "interval index" refers to a measure of an index that is either normal or abnormal when a certain index meets a certain range.
It should be noted that the health assessment method of the present invention ultimately obtains an objective health assessment score (health gap index), which can only be used as an auxiliary parameter for the user and doctor to refer to, and cannot be independently and directly used as a basis for diagnosing a specific disease.
Aiming at the health evaluation of individuals, the invention provides an HE algorithm which divides the comprehensive evaluation model calculation into two stages: the first stage, utilizing a data envelope analysis method to obtain the optimal score of each evaluated human; and in the second stage, regression analysis is carried out in a feasible domain of the weight by taking the optimal score obtained in the first stage as an ideal point, so that the same weight with the maximum benefit of all evaluated persons is obtained, and the problem of the data envelope analysis model is solved. Through the calculation, multiple indexes can be comprehensively scored on the premise of not introducing a step of manually determining weights, so that the influence of human subjective factors is avoided, and a more comprehensive and objective health assessment score is obtained. Therefore, the invention has good application prospect.
It should be apparent that, in light of the foregoing, various modifications, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
The above-described aspects of the present invention will be described in further detail below with reference to specific embodiments in the form of examples. It should not be understood that the scope of the above subject matter of the present invention is limited to the following examples only. All techniques implemented based on the above description of the invention are within the scope of the invention.
Drawings
Fig. 1 is a schematic flow chart of embodiment 1 of the present invention.
Detailed Description
It should be noted that, in the embodiments, algorithms of steps such as data acquisition, transmission, storage, and processing, which are not specifically described, and hardware structures, circuit connections, and the like, which are not specifically described may be implemented through the disclosure of the prior art.
Example 1 health assessment method and System
The system provided in this embodiment includes:
the input module is used for inputting the index of the subject;
the calculation module is used for calculating the health evaluation result of the subject;
and the output module is used for outputting the calculation result of the calculation module.
Based on the above system, the flow of the method for performing health assessment of a subject in this embodiment is shown in fig. 1, and specifically:
relevant indexes of the subjects are collected, including six aspects of genes, physiology, psychology, motion, nutrition and environment, wherein the indexes comprise 1525 indexes and 5946 indexes of the monogenic genetic diseases. The index collection here is performed with a comprehensive and systematic object.
The specific index system for evaluating the health of the tested person is shown in the following table:
TABLE 1 health assessment index System
| First level index | Second level index | Three-level index | |
| Physiology of physiology | 4 | 81 | 891 |
| Gene | 4 | - | 5946 |
| Psychological process | 5 | 50 | 309 |
| Nutritional products | 2 | 9 | 95 |
| Exercise machine | 4 | 15 | 85 |
| Environment (environment) | 3 | 23 | 144 |
| Totalizing | 22 | 178 | 7470 |
In order to scientifically evaluate the influence and the effect of indexes on the health condition in dynamic change, an HE model is adopted. Compared with the traditional method of manually determining the weight, the method has the advantages that the weight is calculated through a model, and a score is given, namely, the method is completely data-driven. The specific steps of obtaining the health gap index of the subject by the algorithm model comprise:
the first step: data preprocessing
(1) The negative index (i.e., the smaller the better the index) is first inverted and the original index data is replaced. And performing the inverse operation on various indexes of the environment. Finally, the dimension is eliminated by an average normalization method, which is to readjust the data range and scale to the range of [0,1 ]. The general formula is as follows:
wherein x' is the index after transformation, x is the index before transformation, mu is the average value of the index, x max Is the maximum value of the index, x min Is the minimum value of the index.
And finally, the processed data is conveniently input into an algorithm model for calculation.
(2) In addition, since physical examination index data is relatively special, most of the physical examination index data is an interval index, that is, a measurement standard of an index is usually normal or abnormal when a certain index satisfies a certain range. Therefore, the following special pretreatment is performed on the physical examination index data.
Assuming that when a certain index shows normal conditions are satisfied with x epsilon a, b, the following transformation is performed on the index:
wherein x is i ' is a transformed index, x i Is an index before transformation;
and a second step of: the ideal value maxV is calculated assuming that each index is calculated according to the optimal weight j .
Wherein maxV j For the ideal value, y ij Is the ith index of the jth individual inspector, w i Is the weight of the ith index, w i+a Is the weight of the (i+a) th index, w r Is the weight of the r index, m is the number of indexes, n is the number of evaluated people, b a Is a constant between 0 and 1 which can be customized, and is used for adding prior knowledge w about weight when a certain weight needs to be customized i -w i+a ≥b a S.t. represents a constraint;
and a third step of:
the second step is to calculate each index according to its own optimal weightFor the ideal value of the j-th evaluation unit, vector +.>Is an ideal point, since this is difficult to achieve in practical applications, our goal is to find a point V from ideal * (w) V (w) closest to the (w). For this purpose we need a distance function for measuring the distance between them. The closest V (w) to the ideal value is obtained when this distance is the smallest. We can therefore calculate the weight w= (w) by minimizing the distance function 1 ,…,w m )。
s.t.w∈W
Wherein min is minimum value operation, D 2 As a distance function of 2-range, V (w) is the distance from the ideal point V * (W) the closest point, W being the set of ownership weights,is maxV obtained in step c j . The other variables are defined as in the previous step.
To use the HE model, the data must satisfy the following principles:
(1) the data must be positive;
(2) the larger the output data must be, the better;
(3) the index must be representative and of importance.
After the health gap index (1-100 points) is calculated, grading is carried out by the following threshold dividing mode:
five star grade: the index gap index is more than or equal to 90, and all indexes in the same age are normal;
four star stages: the 90 index gap index is more than or equal to 75, and part of indexes are abnormal, but normal life and work are not affected;
three star stages: 75> index gap index is more than or equal to 60, and partial index is abnormal, so that the work is influenced;
two star stages: 60> the index gap index is more than or equal to 45, and part of indexes are abnormal, so that the medicine needs to be taken, and the work and the life are affected;
a star grade: 45> index gap index, and needs to be hospitalized to hospitals at irregular intervals.
As an example, 45 common indexes are selected for experiments, including 19 physiological indexes, 0 gene indexes, 4 psychological indexes, 9 nutritional indexes, 10 sports indexes and 3 environmental indexes. Physical examination index information of 50 physical examination persons was collected, of which 25 men and 25 women. The data format is shown in the following table:
table 2 experimental data format
In the above table, the quantitative methods of pesticide pollution, atmospheric pollution and soil pollution are as follows: the subject was assigned a value of 1 when contaminated with the term and a value of 0 when not contaminated with the term.
The HE algorithm model is input according to the data format, the weight of each index can be directly obtained through data driving calculation, the process of determining the weight does not need human participation, and the obtained weight is shown in the following table:
table 3 weights of the indices
The final score and health grade of each physical examination person are calculated through the weight obtained by the algorithm, and the final result is shown in the following table:
table 4 Experimental results
| Physical examination person | Score of | Star grade |
| Physical examination person 2 | 0.999999979 | Five stars |
| Physical examination person 4 | 0.987332632 | Five stars |
| Physical examination person 8 | 0.985760297 | Five stars |
| Physical examination person 9 | 0.983248777 | Five stars |
| Physical examination person 21 | 0.978555832 | Five stars |
| Physical examination person 5 | 0.977107164 | Five stars |
| Physical examination person 24 | 0.973747018 | Five stars |
| Physical examination person 13 | 0.973346324 | Five stars |
| Physical examination person 11 | 0.973149123 | Five stars |
| Physical examination person 7 | 0.961049773 | Five stars |
| Physical examination person 6 | 0.957013539 | Five stars |
| Physical examination person 15 | 0.943922458 | Five stars |
| Physical examination person 22 | 0.930948597 | Five stars |
| Physical examination person 18 | 0.92647623 | Five stars |
| Physical examination person 17 | 0.921797046 | Five stars |
| Physical examination person 20 | 0.916345704 | Five stars |
| Physical examination person 19 | 0.916129007 | Five stars |
| Physical examination person 10 | 0.908976003 | Five stars |
| Physical examination person 1 | 0.90743439 | Five stars |
| Physical examination person 23 | 0.882876812 | Four stars |
| Physical examination person 12 | 0.875160523 | Four stars |
| Physical examination person 14 | 0.86806093 | Four stars |
| Physical examination person 16 | 0.852761555 | Four stars |
| Physical examination person 3 | 0.846589336 | Four stars |
| Physical examination person 25 | 0.749603358 | Two stars |
Compared with the traditional method of manually determining weights, the HE model provided by the embodiment has the advantages that the weights are calculated through the model, and scores are given, namely, the method of complete data driving.
According to the embodiment, the HE algorithm model is constructed, the influence of subjective factors can be eliminated in health evaluation, the result is more comprehensive and objective, and the HE algorithm model has a good application prospect.
Claims (9)
1. The intelligent evaluation method for the health star level is characterized by comprising the following steps of:
step 1, collecting and inputting indexes of a subject;
step 2, calculating the index by adopting an HE algorithm model to obtain a health gap index of the subject;
the calculation process of the HE algorithm model comprises the following steps:
step a, carrying out pretreatment of negation and normalization on negative indexes in the indexes;
step b, preprocessing section indexes in the indexes as follows:
assuming that when a certain index shows normal conditions are satisfied with x epsilon a, b, the following transformation is performed on the index:
wherein x' i As the index after transformation, x i Is an index before transformation;
step c, assuming that each health index is calculated according to the optimal weight, calculating an ideal value, wherein the calculation formula is as follows:
wherein maxV j For the ideal value, y ij Is the ith index of the jth individual inspector, w i Is the weight of the ith index, w i+a Is the weight of the (i+a) th index, w r Is the weight of the r index, m is the number of indexes, n is the number of evaluated people, b a Is a constant between 0 and 1 which can be customized, and is used for adding prior knowledge w about weight when a certain weight needs to be customized i -w i+a ≥b a S.t. represents a constraint;
step d, finding a value V from the ideal value * (w) V (w) nearest to the nearest V (w), the calculation formula is:
wherein min is minimum value operation, D 2 As a distance function of 2-range, V (w) is the distance from the ideal point V * (W) the nearest point, W is the set of ownership weights, V j * Is maxV obtained in step c j 。
2. The healthy star-level intelligent assessment method according to claim 1, wherein: the negative index is inverted by taking the reciprocal and replacing the original index data with the reciprocal.
3. The healthy star-level intelligent assessment method according to claim 1, wherein: the normalization method of the index comprises the following steps:
wherein x' is the index after transformation, x is the index before transformation, mu is the average value of the index, x max Is the maximum value of the index, x min Is the minimum value of the index.
4. According to the weightThe healthy star-level intelligent assessment method as claimed in claim 1, characterized in that: when the weight of the index needs to be manually adjusted, the prior knowledge w about the weight is added into the constraint condition i -w i+a ≥b a 。
5. The healthy star-grade intelligent assessment method according to claim 1, wherein the category of the index includes at least one of the following indexes: is used for evaluating indexes of genes, physiology, psychology, sports, nutrition and environment.
6. The healthy star-grade intelligent assessment method according to claim 5, wherein the index for assessing physiology comprises at least one of the following: hemoglobin, red blood cells, white blood cells, platelet count, fasting blood glucose, serum total cholesterol, serum triacylglycerol, uric acid, troponin I, creatinine, alanine aminotransferase, serum total bilirubin, serum-bound bilirubin, serum-unbound bilirubin, carcinoembryonic antigen, alpha fetoprotein, rheumatoid factor, reactive protein, procalcitonin;
the index for evaluating psychology includes at least one of the following indexes: intellectual, he, anxiety, depression, etc. self-rating;
the index for evaluating nutrition includes at least one of the following indexes: sodium, potassium, magnesium, calcium, phosphorus, zinc, iodine, selenium, manganese;
the index for evaluating exercise includes at least one of the following indexes: body mass index, pulse, total testosterone in early morning resting state, measurement, plasma cortisol, CD4/CD8, igA, igM, igG, blood ammonia, urine specific gravity;
the index for evaluating the environment includes at least one of the following indexes: pesticide pollution, atmospheric pollution and soil pollution.
7. The healthy star rating intelligent assessment method according to claim 1, wherein the healthy gap index is used for rating according to the following thresholds:
five star grade: the index gap index is more than or equal to 90;
four star stages: the index gap index of 90 is more than or equal to 75;
three star stages: 75> index gap index is more than or equal to 60;
two star stages: the index gap index of 60 is more than or equal to 45;
a star grade: 45> index gap index.
8. A health star-level intelligent assessment system, comprising:
the input module is used for inputting the index of the subject;
a calculation module for performing calculations according to the healthy star grade intelligent assessment method of any one of claims 1-7;
and the output module is used for outputting the calculation result of the calculation module.
9. A computer-readable storage medium having stored thereon: computer program for implementing a healthy star grade intelligent assessment as defined in any one of claims 1-7.
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