CN111815487B - Deep learning-based health education assessment method, device and medium - Google Patents
Deep learning-based health education assessment method, device and medium Download PDFInfo
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Abstract
The invention relates to a deep learning-based health education assessment method, a device and a medium technical scheme, which comprise the following steps: acquiring multiple dimensional data affecting health education projects through distributed acquisition equipment and distributed storage equipment; calculating a plurality of statistical indexes related to health problems according to the plurality of dimensional data; carrying out quantization processing on the multiple dimensional data and constructing panel data; establishing one or more deep learning sequence prediction algorithm models according to the panel data; inputting the statistical index into a deep learning sequence prediction algorithm model, and influencing the probability value of the health education project in a future set time period; and (3) setting a threshold value based on the possible value, outputting a moment point with the possible value larger than the threshold value in a future set time period, and taking the moment point as an optimal time point for developing health education. The beneficial effects of the invention are as follows: and the related data is acquired through the distributed big data, and the quantitative processing and the learning calculation are carried out, so that the health education project is effectively evaluated.
Description
Technical Field
The invention relates to the field of computers, in particular to a health education assessment method, device and medium based on deep learning.
Background
The ideas and practical applications of health education and health promotion hold a vital role in the overall process of health management. The design, implementation and evaluation of a health education program or a health management program is the best mapping of the main implementation steps or measures of health education or health management, such as data collection, demand evaluation, intervention implementation and effect evaluation. Wherein for the design of a health education program, health education diagnosis, also called health education demand assessment, is the first and most important step in the design of a health education program.
The prior art lacks effective technical means for data quantification and evaluation of health education.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art, and provides a health education assessment method, a device and a medium based on deep learning.
The technical scheme of the invention comprises a health education assessment method based on deep learning, which is characterized by comprising the following steps: s100, acquiring multiple dimensional data affecting health education projects through distributed acquisition equipment and distributed storage equipment; s200, calculating a plurality of statistical indexes related to health problems according to a plurality of dimensional data; s300, carrying out quantization processing on a plurality of pieces of dimension data and constructing panel data; s400, one or more deep learning sequence prediction algorithm models are built according to the panel data; s500, inputting the statistical index into the deep learning sequence prediction algorithm model, and influencing the probability value of the health education item in a future set time period; and S600, setting a threshold value based on the possible value, outputting a moment point in a future set time period, wherein the possible value is larger than the threshold value, and taking the moment point as an optimal time point for developing health education.
According to the deep learning-based health education assessment method, the distributed acquisition equipment and the distributed storage equipment are based on the combination of Hadoop, spark and Pyspark distributed frameworks, and the distributed storage equipment adopts Hive distributed storage so as to realize large data environment construction.
The deep learning-based health education assessment method, wherein S100 comprises: data acquisition is carried out on one or more parameters corresponding to life quality data, social environment data, economic environment data, cultural environment data, policy data and social resource data through the distributed acquisition equipment; and calculating one or more indexes corresponding to the life quality data, the social environment data, the economic environment data, the cultural environment data, the policy data and the social resource data according to the parameters.
The deep learning-based health education assessment method, wherein S200 comprises: counting the severity of health problems and the harmfulness thereof, main health problems and main dangerous factors thereof according to the dimension data of the S100; wherein, health problem severity and harm medical statistics index calculation mainly includes: if a health problem has a short course of disease, an acute characteristic or epidemic disease, calculating the morbidity, mortality and fatality rate of the health problem or disease in a target area or crowd in a certain unit time; if a certain health problem has the characteristics of long disease course and difficult cure or is chronic disease, calculating the morbidity, mortality and fatality rate of a certain unit time in a target area or crowd; the calculation of the medical statistics indexes of the main health problems and the main risk factors thereof mainly comprises the steps of calculating the relative risk RR and the ratio OR between the influence factors of a certain health problem OR disease and the disease, and the attribution risk AR, the attribution risk percentage AR and the crowd attribution risk percentage PAR between the two;
the calculation method comprises the following steps:
AR = some influencing factor exposure group incidence-some influencing factor non-exposure group incidence,
the deep learning-based health education assessment method, wherein S200 comprises: performing digital processing on the collected non-digital dimensional data to obtain quantized data, and calculating a prior intervention health problem value, a health problem influence factor value, a health education content value and a health education project possibility value by taking the dimensional data and the quantized data as original data; and constructing panel data according to any one of the time, the quantized data and the prior intervention health problem value, the health problem influence factor value, the health education content value and the health education project possibility value corresponding to the quantized data.
The deep learning-based health education assessment method, wherein S400 includes: corresponding prediction models are established through deep learning time sequence prediction algorithms including but not limited to OpenAR and ARIMA, and the prediction model training and verification data are respectively based on the panel data.
The deep learning-based health education assessment method, wherein S500 comprises: and running and predicting the prior intervention health problem value, the health problem influence factor value, the health education content value and the health education possibility value in a specific future time period in the time sequence prediction model input by the panel data, wherein the prior intervention health problem value, the health problem influence factor value and the health education possibility value are predicted by using but not limited to an OpenAR multi-dimensional time sequence prediction model, and the health education content value is selected by using but not limited to an ARIMA single-dimensional time sequence prediction model.
The deep learning-based health education assessment method according to claim, wherein the method further comprises: recording a group of values with highest priority for intervention of health problem values, health problem influence factor values, health education content values and health education possibility values, and corresponding selected health education development time, health education problems, health problem influence factors and health education evaluation values, and marking by using a one-hot coding principle; when the evaluation information is accumulated to a set number, the data is input into a time series prediction model to perform advanced simulation, and the success degree of the health education project plan is estimated.
The technical scheme of the invention also comprises a health education assessment device based on deep learning, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and is characterized in that any one of the method steps is realized when the processor executes the computer program.
The technical scheme of the invention also comprises a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the method is characterized in that any one of the method steps is realized when the processor executes the computer program.
The beneficial effects of the invention are as follows: based on the theoretical knowledge of epidemiology and medical statistics, the accurate evaluation of health education projects is realized by combining big data with deep learning technology.
Drawings
The invention is further described below with reference to the drawings and examples;
FIG. 1 is a general flow diagram according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a distributed device according to an embodiment of the present invention;
FIG. 3 is a flowchart showing a detailed method for health education assessment according to an embodiment of the present invention;
fig. 4 shows an apparatus and a medium diagram according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to the present embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the accompanying drawings are used to supplement the description of the written description so that one can intuitively and intuitively understand each technical feature and overall technical scheme of the present invention, but not to limit the scope of the present invention.
In the description of the present invention, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number.
In the description of the present invention, the continuous reference numerals of the method steps are used for facilitating examination and understanding, and by combining the overall technical scheme of the present invention and the logic relationships between the steps, the implementation sequence between the steps is adjusted without affecting the technical effect achieved by the technical scheme of the present invention.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement and the like should be construed broadly, and those skilled in the art can reasonably determine the specific meaning of the terms in the present invention in combination with the specific contents of the technical scheme.
Fig. 1 shows an overall flow chart according to an embodiment of the invention, the flow comprising: s100, acquiring multiple dimensional data affecting health education projects through distributed acquisition equipment and distributed storage equipment; s200, calculating a plurality of statistical indexes related to health problems according to the plurality of dimensional data; s300, carrying out quantization processing on the multiple dimensional data and constructing panel data; s400, one or more deep learning sequence prediction algorithm models are built according to panel data; s500, inputting the statistical index into a deep learning sequence prediction algorithm model, and influencing the probability value of the health education project in a future set time period; s600, setting a threshold value based on the possible value, outputting a moment point with the possible value larger than the threshold value in a future set time period, and taking the moment point as an optimal time point for developing health education; s700, inputting any health education project meeting the data requirement of the deep learning sequence prediction algorithm model to obtain a prediction or evaluation result.
Fig. 2 is a schematic diagram of a distributed apparatus according to an embodiment of the present invention, where the distributed apparatus includes a plurality of distributed acquisition devices, and the distributed acquisition devices may be various mobile devices, such as a mobile phone, a notebook, and a mobile medical device, or may be various computer devices with an acquisition function, and the distributed storage devices may be various cloud servers. The big data storage and calculation environment of hive+hadoop+spark+Pyspark is adopted, the Hive distributed storage is adopted, and the Hadoop+spark+Pyspark-based distributed calculation is adopted, so that the big data environment is built.
Fig. 3 is a flowchart of a detailed method for health education assessment according to an embodiment of the present invention, in which only the second, third, seventh and eighth steps are labeled, and the first, fourth, fifth and sixth steps mainly implement data acquisition analysis and prediction model construction, and the detailed method is as follows:
the device is divided into 7 modules: social diagnostics (designated community/group), epidemiological diagnostics, environmental diagnostics, behavioral diagnostics, educational and organizational diagnostics, administrative and policy diagnostics, and priority project diagnostics. The 7 modules are independent and related to each other.
The first module is social diagnosis (appointed community/crowd), and aims to realize social condition and demand assessment of a research target community or crowd. The module comprises 7 sub-modules, respectively: quality of life diagnostics, social environmental diagnostics, economic environmental diagnostics, cultural environmental diagnostics, related policy diagnostics, and social resource diagnostics.
And a second module, epidemiological diagnosis, aiming at researching main health problems affecting life quality and influencing factors thereof and determining prior intervention health problems. The module comprises 3 sub-modules, which are respectively: health problem severity and hazard diagnosis, major health problems and major risk factor diagnosis, and health problems with preferential intervention are established.
Module three, environmental diagnostics (related to specified health problems), aimed at determining environmental factors affecting health and quality of life, and determining environmental factors that prioritize intervention. The module comprises 2 sub-modules, respectively: social factor diagnosis and substance condition factor diagnosis.
And a fourth module, behavior diagnosis, aiming at determining behavior factors influencing health conditions and life quality so as to determine behavior life style of preferential intervention. The module comprises 3 sub-modules, which are respectively: behavioral and non-behavioral factor diagnostics, important behavior and unimportant behavior diagnostics, high variable behavior and low variable behavior diagnostics.
And a fifth module, education and organization diagnosis, which aims to analyze related behaviors and environmental factors affecting health and causing specific health problems and provide effective basis for the specification of health education intervention strategies. The module comprises 3 sub-modules, which are respectively: trend factor diagnosis, contributor factor diagnosis and enhancement factor diagnosis.
And a sixth module, management and policy diagnosis, aiming at evaluating the resources and environment of health education and evaluating the possibility and deep level of implementing the intervention of health education. The module comprises 3 sub-modules, which are respectively: tissue resource diagnosis, external strength diagnosis, and policy environment diagnosis.
And a seventh module, preferentially diagnosing the project, aiming at determining preferentially solved health problems and preferentially intervening behaviors by combining project feasibility and expected effects, thereby determining preferentially realized health education projects. The module has 1 sub-module, which evaluates and screens priority items.
The invention is divided into 8 steps, which are respectively: big data environment construction, data acquisition, epidemiology and medical statistics index calculation, acquisition data quantification and panel data establishment, deep learning-based time sequence prediction algorithm model establishment, operation model and health education project content establishment, priority health education project establishment and priority project assessment.
Step one, constructing a big data environment. According to the invention, specific requirements are set on hardware storage according to the experimental data quantity, and in addition, GPU hardware calculation is required for calculation. The step adopts a hive+hadoop+spark+Pyspark big data storage and computing environment, adopts Hive distributed storage, and adopts Hadoop+spark+Pyspark based distributed computing to realize the construction of the big data environment.
Step two, data acquisition, wherein the acquired data content includes but is not limited to:
data acquisition for module one:
quality of life related data (e.g., average consumption level, average level of resident income, resident living condition, average number of living houses per square meter, enrolment factor) of the city/region to which the present community or group of people belongs;
social environment-related data (e.g., the religious popularity of the community or crowd, the preference of residents for something due to the weather, the acceptance of new things or lifestyles by residents, the knowledge of health education by residents, the knowledge of health management by residents, the popularity of the concept of "personal health"), etc.);
economic environment related data (e.g., GDP of the city/region to which the community or crowd belongs, average consumption level, ratio of non-agricultural economic income to total income, urban resident dominant income, regional production total value, third industry added value, total social fixed asset investment, total social consumer goods retail amount, regional production total increase, ratio of three-and two-product production value, financial institution deposit, total number of 500 strong enterprise headquarters in China);
cultural environment related data (e.g., number of universities in target area 985/211, number of patent applications/issued, number of national key laboratories, number of R & D investments per year, number of non-material cultural heritage, favorite and preference levels of residents for healthy foods due to religious beliefs, acceptance of new things or lifestyles by residents due to religious beliefs);
policy-related data (e.g., including population specific gravity, number of times of annual development of resident health files and specific gravity of resident in the arrival, popularity of preventive inoculation, number of times and specific gravity of annual family follow-up for neonatal health management, number and specific gravity of annual follow-up for puerperal post-treatment, free measurement of blood pressure and number and specific gravity of annual follow-up for patients with hypertension of 35 years old, free fasting blood glucose detection and number and specific gravity of annual follow-up for patients with diabetes of type II, free health check and specific gravity of annual free for severe psychotic patients, specific gravity of health management for tuberculosis patients, etc.);
social resource-related data (e.g., target area human resource level assessment (including the proportion of incumbent staff to adults, the proportion of preschool staff above the scholar to total incumbent staff, etc.), government subsidized total amount for health).
Data acquisition for module two:
calculating health problem severity and data related to the need for harm (e.g., statistics of health problems or diseases recorded by community health service stations, statistics of such health problems or diseases for which the frequency and number of feedback of the health problems or diseases by residents in the target area each year, the number of diagnosed people of the health problems or diseases, the awareness rate, treatment rate and control rate of the health problems or diseases, the disability rate of the health problems or diseases, the mortality rate of the health problems or diseases, the average degree of influence of the health problems or diseases on the daily life of the diagnosed patients);
calculating data related to health problems and their major risk factors (e.g., based on health problems or diseases recorded by the community health service stations described above, statistics of data for which the health problems or diseases are due to genetic factors, the health problems or diseases are due to smoking, the health problems or diseases are due to insufficient physical activity, the health problems or diseases are due to viral infection, the health problems or diseases are due to dietary factors, the health problems or diseases are due to obesity or overweight, the health problems or diseases are due to hypertension, and the health problems or diseases are due to household factors).
Data acquisition for module three:
social factor related data (the part includes the social factor related data collection of the health problem and the disease based on the collected health problem and disease type, for example, the number of regulations of the target community contributing to the prevention of the health problem or the disease, the number of measure projects of the target community contributing to the prevention of the health problem or the disease, and the measure effectiveness of the target community contributing to the prevention of the health problem or the disease);
substance condition factor-related data (the part including the collection of substance condition factor-related data for realizing a health problem and a disease based on the above-mentioned collected health problem and disease type: for example, a target community has a number of institutions diagnosing the health problem or disease, a mean living environment index for the target community contributing to the prevention of the health problem or disease, a mean working environment index for the target community contributing to the prevention of the health problem or disease);
data acquisition for module four:
the part comprises the step of realizing the data collection related to the initiation or pathogenicity factors of the individual health problems or diseases based on the collected health problems and disease types. These factors were then scored for behavioral factor health management based on the following criteria:
behavioral and non-behavioral diagnosis (judging whether the cause or causative factor of a given health problem or disease is a behavioral factor (such as improper diet) or a non-behavioral factor (such as genetic tendency), if the behavioral factor is diagnosed, the behavioral factor health management score is scored as 5, and if the non-behavioral factor is diagnosed, the behavioral factor health management score is scored as 1);
important behavior and unimportant behavior diagnosis (judging whether a behavior factor causing or causing a specified health problem or disease belongs to an important behavior factor (such as a frequently occurring behavior or a direct causal relationship between the behavior and the specified health problem or disease) or a non-important behavior factor (such that the behavior is not closely related or frequently related to the specified health problem or disease), if the importance is diagnosed, the behavior factor health management score is multiplied by 10 times, and if the importance is diagnosed, the non-important behavior is not changed);
diagnosis of high variable behavior and low variable behavior (judging whether the causative or causative behavior factor of a specified health problem or disease belongs to a variable behavior factor (such as behavior in the period of just forming or developing, evidence of successful changes in other plans, social disapproval behavior) or a non-variable behavior factor (such as long forming time, root-planted in cultural tradition or traditional lifestyle, unsuccessful change in the past), if high variable behavior is diagnosed, the behavior factor health management score is multiplied by 10, and if low variable behavior is diagnosed, divided by 10).
Data acquisition for module five:
the part comprises the step of counting different health behaviors and influence factors thereof for members with health management in a target community or crowd. This factor can be used as an educational direction for specifying a health education program, and these factors are classified into the following three categories:
trend factor diagnosis (e.g., a person has a strong change intention), and if a trend factor is diagnosed, the factor health education importance score is scored as 2;
factor diagnosis (e.g., individuals have good personal care skills) if a factor is diagnosed as a predisposing factor, the factor health education importance score is scored as 1;
if the factor is diagnosed as a trend factor, the factor health education importance score is marked as 3;
data acquisition for module six:
the organization resource diagnosis comprises the internal strength of the organization resource for developing the health education, such as the number of organization people, funds, whether the place is determined, the experience level of personnel, the number of health managers in the organization and the like;
external force diagnosis including the support strength of the external to the health education, such as sponsored money, government money amount, etc.;
policy environmental diagnosis including support and affirmation of the health education work by local government and health departments.
And thirdly, epidemiology and medical statistics index calculation, wherein the step is based on the second data collection, and the index calculation is realized by combining the epidemiology medical statistics theory, and the related modules comprise the second health problem severity and harm module and the medical statistics index calculation of main health problems and main dangerous factors thereof.
Epidemiological and medical statistics for module two are calculated as follows:
based on the acquired data, the step realizes the statistical calculation of the severity and the harm of the health problem, the main health problem and the main dangerous factors thereof.
The health problem severity and the harm medical statistics index calculation mainly comprise: if a health problem has a short course of disease, an acute characteristic or epidemic disease, calculating the morbidity, mortality and fatality rate of the health problem or disease in a target area or crowd in a certain unit time; if a health problem has the characteristics of long disease course, difficult cure or chronic disease, the morbidity, mortality and mortality of the target area or crowd in a certain unit time are calculated.
The calculation of the medical statistics index of the main health problem and the main risk factors mainly comprises the calculation of the Relative Risk (RR) and the ratio (OR) between the influence factors of a certain health problem OR disease and the disease, and the Attribution Risk (AR), attribution risk percentage (AR) and crowd attribution risk percentage (PAR%) between the two. The calculation formula is as follows:
RR = incidence of a factor-exposed group/incidence of a factor-non-exposed group
OR = number of exposed groups in case group/number of non-exposed groups in case group-number of exposed groups in control group/number of non-exposed groups in control group
AR = incidence of a factor-exposed group-incidence of a factor-non-exposed group
AR% = AR/incidence of a factor-affected exposed group x 100%
PAR％＝(P(RR-1))/(P(RR-1)+1)×100％
Step four, quantification of collected data and establishment of panel data
The step includes the quantification of the data collected in the step two and the establishment of the panel data.
The quantization process includes digitizing the acquired non-digital data, such as: the organization resource diagnosis collects data "whether a place is determined", replaces data of "yes" with "1", and replaces data of "no" with "0".
The invention takes the collected data and the quantized data as the original data, and calculates the prior intervention health problem value, the health problem influence factor value, the health education content value and the health education project possibility value based on the original data.
Preferential intervention the health problem value is to evaluate how much a health problem is preferentially intervened, the higher the value, the more should the health problem be preferentially intervened. The index is calculated by regularizing the index of dimension corresponding to the X axis of the table related to the specific health problem, calculating the information entropy of regularized data of each index and calculating the average value as the prior intervention health problem value of the health problem. Table 1 of panel data is built based on the following data, i.e., X-axis is time; the Y-axis is information entropy data of the following indexes: quality of life related data, social environment related data, economic environment related data, cultural environment related data, related policy related data and social resource related data, social factor related data and matter condition factor related data, health problem severity and hazard related data, morbidity/morbidity, mortality; the Z-axis is the value of the priority intervention health problem.
In table 1, the health problem influence factor values of the priority intervention health problem value calculation rule and the corresponding panel data establishment method are used for evaluating the influence degree of the specified health influence factor on the specific health problem, and the greater the health problem influence factor value is, the higher the influence degree of the factor on the specified health problem is. The index calculating method comprises the steps of regularizing the performance factor health management score, the relative risk and ratio between the influence factors of the appointed health problem or the disease and the disease, the attribution risk percentage and the crowd attribution risk percentage, solving the information entropy of the regularized result, and obtaining the average value of the information entropy result to be the health problem influence factor value of the health influence factor on the appointed health problem. Table 2 of panel data is built based on the following data, i.e., X-axis is time; the Y axis is the performance factor health management score, the relative risk and ratio of the influence factors of the appointed health problem or the disease to the disease, the attribution risk percentage and the crowd attribution risk percentage; the Z axis is the health problem influencing factor value.
Table 2 health problem influencing factor value calculation rule and corresponding panel data creation method health education content value is to evaluate the degree of acceptance of a certain health education content by an educator, and the higher the health education content value is, the more acceptable the educator can accept the health education content. The calculation method of the index is as follows: regularization processing is carried out on the health education importance scoring data of the appointed factors, and the regularization result is used for solving information entropy of the health education importance scoring data, wherein the information entropy is the health education content value of the health influence factors on the appointed health problems. Creating a panel data table 3 based on the following data, i.e. the X-axis is time; the Y-axis is health education importance scoring data of the appointed factors; the Z-axis is the health education content value.
| X-axis | Time |
| Y-axis | Health education importance scoring data specifying factors |
| Z-axis | Health education content value |
Table 3 health education content value calculation rule and corresponding panel data creation method health education possibility value is to evaluate the degree of possibility of holding this health education, the larger the index is, the more health education can be realized under limited resources and conditions. The calculation rule of the index is as follows: the method comprises the steps of carrying out regularization treatment on life quality related data, social environment related data, economic environment related data, cultural environment related data, related policy related data, social resource related data, social factor related data, material condition factor related data, organization resource related data, external strength related data and policy environment related data, then carrying out information entropy calculation on regularized results, and obtaining index information entropy average value as a health education possibility value. Creating a panel data table 4 based on the following data, namely, the X axis is time; the Y-axis is life quality related data, social environment related data, economic environment related data, cultural environment related data, related policy related data and social resource related data, social factor related data and matter condition factor related data, organization resource related data, external strength related data, and policy environment related data; the Z-axis is the health education likelihood value.
Table 4 health education likelihood value calculation rule and corresponding panel data creation method
Step five, time sequence prediction algorithm model establishment based on deep learning
The invention adopts a deep learning time sequence prediction algorithm which is not limited to OpenAR and ARIMA to establish four prediction models. And (3) building four prediction model training and verification data based on the four panel data obtained in the step (4), wherein the input nodes are all Y-axis data, and the output nodes are all Z-axis data. The OpenAR is widely applied to multi-dimensional time series prediction, and the ARIMA is applied to single-dimensional time series prediction.
Step six, running the model and establishing the content of the health education project,
the method inputs the step tetrahedral plate data into the time sequence prediction model established in the step five, and runs and predicts the prior intervention health problem value, the health problem influence factor value, the health education content value and the health education possibility value in a specific time period in the future. The prediction of the health problem value, the health problem influencing factor value and the health education possibility value is selected from but not limited to a time sequence prediction model of multiple OpenARs, and the health education content value is selected from but not limited to a time sequence prediction model of single dimension of ARIMA.
And step seven, establishing a priority health education project.
A threshold value is set for the health education possibility value, a time point (the time can be month or day) when the health education possibility value is larger than the threshold value in a specific future time period (the time can be one year or half year or the like) is output, and the time point when the health education possibility value is maximum is selected as the optimal time point for developing the health education.
Selecting a series of health problems with highest priority intervention health problem values, a group of health problem influence factors with highest corresponding health problem influence factor values, and a series of health education contents with highest health education content values as key points of the health education.
Step eight, priority item assessment
When the method is used for the first time, a group of values which are selected in the step seven and have the most proper (highest) values of the health problem values, the health problem influence factor values, the health education content values and the health education possibility values and the corresponding selected health education development time, health education problems and health problem influence factors, namely the health education content, are recorded, and marked by adopting a 'one-hot' coding principle (0/1 principle). And evaluating the health education plan developed at this time after the health education is in war.
And when the evaluation information is accumulated to a certain amount, inputting the data into the multiple exclusive time sequence models established in the step five, realizing advanced simulation, and evaluating the success degree of the current optimal project plan.
Fig. 4 shows an apparatus and a medium diagram according to an embodiment of the invention. Fig. 4 shows a schematic view of an apparatus according to an embodiment of the invention. The apparatus comprises a memory 100 and a processor 200, wherein the processor 200 stores a computer program for executing: acquiring multiple dimensional data affecting health education projects through distributed acquisition equipment and distributed storage equipment; calculating a plurality of statistical indexes related to health problems according to the plurality of dimensional data; carrying out quantization processing on the multiple dimensional data and constructing panel data; establishing one or more deep learning sequence prediction algorithm models according to the panel data; inputting the statistical index into a deep learning sequence prediction algorithm model, and influencing the probability value of the health education project in a future set time period; and (3) setting a threshold value based on the possible value, outputting a moment point with the possible value larger than the threshold value in a future set time period, and taking the moment point as an optimal time point for developing health education. Wherein the memory 100 is used for storing data.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present invention.
Claims (7)
1. A method for health education assessment based on deep learning, the method comprising:
s100, acquiring multiple dimensional data affecting health education projects through distributed acquisition equipment and distributed storage equipment;
s200, calculating a plurality of statistical indexes related to health problems according to a plurality of dimensional data;
s300, carrying out quantization processing on a plurality of pieces of dimension data and constructing panel data;
s400, one or more deep learning time sequence prediction algorithm models are built according to the panel data;
s500, inputting the panel data into the deep learning time series prediction algorithm model;
s600, selecting a series of health problems with highest priority intervention health problem values, a group of health problem influence factors with highest corresponding health problem influence factor values, and a series of health education contents with highest health education content values as key points of health education;
wherein, the S100 includes:
one or more parameters corresponding to life quality data, social environment data, economic environment data, cultural environment data, policy data and social resource data are acquired through the distributed acquisition equipment;
calculating one or more indexes corresponding to life quality data, social environment data, economic environment data, cultural environment data, policy data and social resource data according to the parameters;
the S200 includes:
counting the severity of health problems and the harmfulness thereof, main health problems and main dangerous factors thereof according to the dimension data of the S100;
wherein, health problem severity and harm medical statistics index calculation includes: if a health problem has a short course of disease, an acute characteristic or epidemic disease, calculating the morbidity, mortality and fatality rate of the health problem or disease in a target area or crowd in a certain unit time; if a certain health problem has the characteristics of long disease course and difficult cure or is chronic disease, calculating the morbidity, mortality and fatality rate of a certain unit time in a target area or crowd;
the calculation of the medical statistics indexes of the main health problems and the main risk factors thereof comprises the steps of calculating the relative risk RR and the ratio OR between the influence factors of a certain health problem OR disease and the disease, and the attribution risk AR, attribution risk percentage AR and crowd attribution risk percentage PAR between the two;
the calculation method comprises the following steps:
，
，
，
，
；
the S300 includes:
the acquired non-digital dimension data is subjected to digital processing to obtain quantized data, the dimension data and the quantized data are used as raw data to calculate a prior intervention health problem value, a health problem influence factor value and a health education content value, wherein the prior intervention health problem value is used for evaluating the prior intervention degree of a health problem, the health problem influence factor value is used for evaluating the influence degree of a specified health influence factor on a specific health problem, and the health education content value is used for evaluating the acceptance degree of an educator on certain health education content;
constructing panel data according to time, the quantized data and any value of a health problem value, a health problem influence factor value and a health education content value which are preferentially interfered corresponding to the quantized data;
wherein, the prior intervention health problem value is obtained according to the following steps:
regularizing life quality related data, social environment related data, economic environment related data, cultural environment related data, related policy related data, social resource related data, social factor related data, material condition factor related data, health problem severity and harm related data, morbidity or morbidity, mortality and fatality rate respectively to obtain corresponding first regularized data;
calculating the information entropy of each first regularized data, calculating the first average value of the information entropy of all the first regularized data, and determining the first average value as the prior intervention health problem value;
wherein, the health problem influence factor value is obtained according to the following steps:
regularizing the health management scores of the behavior factors, the relative dangers and ratio ratios between the influence factors of the appointed health problems or diseases and the corresponding diseases, the attribution dangers percentage and the crowd attribution dangers percentage respectively to obtain corresponding second regularized data;
calculating the information entropy of each second regularized data, calculating the second average value of the information entropy of all the second regularized data, and determining the second average value as the prior intervention health problem value;
wherein, the health education content value is obtained according to the following steps:
regularization processing is carried out on the health education importance scoring data of the appointed factors, third regularized data are obtained, and information entropy corresponding to the third regularized data is determined to be the health education content value.
2. The deep learning-based health education assessment method according to claim 1, wherein the distributed acquisition device and the distributed storage device are based on a combination of distributed frameworks of Hadoop, spark and pypack, and the distributed storage device adopts Hive distributed storage to realize big data environment construction.
3. The deep learning based health education assessment method according to claim 1, wherein the S400 includes:
and establishing a corresponding prediction model by using an OpenAR and ARIMA deep learning time sequence prediction algorithm, wherein the prediction model training and verification data are respectively based on the panel data.
4. The deep learning based health education assessment method as claimed in claim 3, wherein the S500 includes:
and running and predicting a prior intervention health problem value, a health problem influence factor value and a health education content value in a specific future time period in the time sequence prediction model input by the panel data, wherein the prior intervention health problem value and the health problem influence factor value are predicted by using an OpenAR multi-dimensional time sequence prediction model, and the health education content value is selected by using an ARIMA single-dimensional time sequence prediction model.
5. The deep learning based health education assessment method as claimed in claim 4, further comprising:
recording a group of values with highest values of health problem values, health problem influence factor values and health education content values which are interfered with preferentially, selecting corresponding health education problems, health problem influence factors and health education content, marking by a one-hot coding principle, and evaluating a health education project plan;
when the evaluation information is accumulated to a set number, the data is input into a time series prediction model to perform advanced simulation, and the success degree of the health education project plan is estimated.
6. A deep learning based health education assessment device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method steps of any one of claims 1-5 when executing the computer program.
7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method steps of any of claims 1-5.
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