CN119905265A - A health examination data management method and system - Google Patents
A health examination data management method and system Download PDFInfo
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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
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
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- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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Abstract
The invention discloses a health physical examination data management method and system, wherein the method comprises the steps of obtaining health physical examination data of a user, wherein the health physical examination data comprise physiological indexes, medical history information, physical examination results and life habit data of the user, intelligently evaluating the health physical examination data by using a deep learning algorithm to obtain the health state of the user, calculating comprehensive health risk indexes of the user according to the health physical examination data, and generating a health report of the user based on the health state, the comprehensive health risk indexes and the health physical examination data so as to realize management of the health physical examination data. By utilizing the embodiment of the invention, comprehensive and accurate health risk assessment can be realized, and a more accurate and personalized health management scheme is provided.
Description
Technical Field
The invention belongs to the technical field of data management, and particularly relates to a health physical examination data management method and system.
Background
With the continuous progress of modern medical technology, people's attention to health is gradually increasing, and health physical examination has become a health management means for many individuals and families to perform regularly. The health examination can help to find potential health problems in early stage, and can provide important basis for subsequent health management through systematic data collection and analysis. Traditional health physical examination mainly comprises measurement of individual physiological indexes, review of medical history, analysis of physical examination results, inquiry of living habits and the like. However, the existing health physical examination data management method has a certain limitation, the potential of health data cannot be fully mined, and dynamic monitoring and personalized recommendation of health conditions cannot be realized.
Disclosure of Invention
The invention aims to provide a health physical examination data management method and system, which solve the defects in the prior art, can realize comprehensive and accurate health risk assessment and provide a more accurate and personalized health management scheme.
One embodiment of the present application provides a health physical examination data management method, which includes:
Acquiring health physical examination data of a user, wherein the health physical examination data comprise physiological indexes, medical history information, physical examination results and life habit data of the user;
Performing intelligent evaluation on the health physical examination data by using a deep learning algorithm to obtain the health state of the user;
calculating the comprehensive health risk index of the user according to the health physical examination data;
and generating a health report of the user based on the health state, the comprehensive health risk index and the health physical examination data so as to realize management of the health physical examination data.
Optionally, the performing intelligent evaluation on the health physical examination data by using a deep learning algorithm to obtain the health state of the user includes:
Extracting key index features associated with assessing the health state of the user from the health physical examination data;
And inputting the key index features into a pre-trained health state assessment model based on a deep learning algorithm, and outputting the health state of the user.
Optionally, the calculation formula of the comprehensive health risk index is:
Wherein the said For an integrated health risk index, theA weight coefficient for the ith health index, theAn absolute value of a difference between the current value of the ith health index and a preset ideal value, theReflecting the relationship between the index and the health risk for the sensitivity coefficient of the ith health index, saidA trend coefficient of the ith index, representing the trend of the change of the health index, saidThe risk index is the j-th external risk factor, n is the number of health indexes, and m is the number of external risk factors.
Optionally, the health report includes health status of the user, comprehensive health risk index, dynamic change of various physiological indexes, disease risk assessment and health management advice.
Yet another embodiment of the present application provides a health examination data management system, the system comprising:
The acquisition module is used for acquiring health physical examination data of the user, wherein the health physical examination data comprise physiological indexes, medical history information, physical examination results and life habit data of the user;
The evaluation module is used for intelligently evaluating the health physical examination data by using a deep learning algorithm to obtain the health state of the user;
the calculation module is used for calculating the comprehensive health risk index of the user according to the health physical examination data;
and the generation module is used for generating a health report of the user based on the health state, the comprehensive health risk index and the health physical examination data so as to realize management of the health physical examination data.
A further embodiment of the application provides a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of the preceding claims when run.
Yet another embodiment of the application provides an electronic device comprising a memory having a computer program stored therein and a processor configured to run the computer program to perform the method recited in any of the preceding claims.
Compared with the prior art, the health physical examination data management method provided by the invention is used for acquiring the health physical examination data of the user, wherein the health physical examination data comprise the physiological indexes, the medical history information, the physical examination results and the life habit data of the user, intelligently evaluating the health physical examination data by using a deep learning algorithm to obtain the health state of the user, calculating the comprehensive health risk index of the user according to the health physical examination data, and generating the health report of the user based on the health state, the comprehensive health risk index and the health physical examination data so as to realize the management of the health physical examination data, thereby realizing comprehensive and accurate health risk evaluation and providing a more accurate and personalized health management scheme.
Drawings
Fig. 1 is a hardware block diagram of a computer terminal of a health examination data management method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a health examination data management method according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of a health examination data management system according to an embodiment of the present invention.
Detailed Description
The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
The embodiment of the invention firstly provides a health physical examination data management method which can be applied to electronic equipment such as a computer terminal, in particular to a common computer and the like.
The following describes the operation of the computer terminal in detail by taking it as an example. Fig. 1 is a block diagram of a hardware structure of a computer terminal of a health examination data management method according to an embodiment of the present invention. As shown in fig. 1, the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program comprises program instructions that, when executed, cause the processor to perform any of a number of health check data management methods.
The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by a processor, causes the processor to perform any of a number of health check data management methods.
The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the architecture shown in fig. 1 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements may be implemented, as a particular computer device may include more or less components than those shown, or may be combined with some components, or may have a different arrangement of components.
It should be appreciated that the Processor may be a central processing unit (Central Processing Unit, CPU), it may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Referring to fig. 2, an embodiment of the present invention provides a health examination data management method, which may include the following steps:
s201, acquiring health physical examination data of a user, wherein the health physical examination data comprise physiological indexes, medical history information, physical examination results and life habit data of the user;
Acquiring health physical examination data of a user is the basis of a health management system. The health physical examination data comprise four types of core information, namely physiological indexes, medical history information, physical examination results and life habit data. The physiological index refers to various basic health parameters of the body, such as blood pressure, heart rate, body temperature, blood sugar, weight, height and the like, which are obtained through conventional physical examination, and the data can reflect the current health condition of an individual. Medical history information includes past records of the user's disease, family genetic history, past surgical history, known chronic disease, etc., which helps the physician assess potential health risks. The physical examination result refers to specific examination data obtained by physical examination, such as blood examination, urine examination, imaging examination (such as X-ray and CT scanning result), etc., and helps to identify whether some potential health problems or diseases exist. The life habit data comprise the conditions of eating habits, exercise amount, sleep quality, smoking and drinking of the user, and the like, and reflect the influence of the life mode on health. These data are collected in a variety of ways, such as by way of smart devices, health management platforms, personal information entered by the user, or diagnostic records of the doctor.
By comprehensively collecting health physical examination data of the user, sufficient basic information can be provided for subsequent health assessment and management. The comprehensive analysis of various data is not only beneficial to accurately evaluating the current health condition of the user, but also provides necessary parameters for calculating health risks. The change of the physiological index can directly reflect the health trend of an individual, the medical history information provides a history basis for analyzing health risks, the physical examination result helps to identify potential diseases, and the life habit data reveals the relationship between the life style and health of the user. By combining the data, the health management system can carry out multidimensional and comprehensive evaluation on the health state of the user, thereby providing personalized and accurate health advice and finally realizing intelligent management on health physical examination data.
In order to realize comprehensive management of health physical examination data, the health physical examination data of a user needs to be acquired in various modes. Specifically, these data mainly include physiological indexes, medical history information, physical examination results, and lifestyle data. The first step is data collection through the intelligent device and the health management system. For example, a smart watch or a bracelet worn by a user can record physiological indexes such as heart rate, step number, sleep quality, blood oxygen saturation and the like in real time, and synchronously update with a mobile phone or a health management platform through Bluetooth or Wi-Fi. Information such as weight, height, body fat rate and the like of the user can be collected through the intelligent weighing scale and automatically uploaded to the health management system. The physiological data can accurately reflect the daily health condition of the user and provide an effective basis for subsequent analysis.
Meanwhile, medical history information needs to be manually filled in by a user, imported by a medical institution or updated through an online health platform. For example, when registering a health management account, the user needs to fill in a personal health profile, including known chronic diseases, family history, past records of major diseases, and so forth. If the user has undergone an examination at some medical facility, the data may be interfaced with an Electronic Health Record (EHR) system of the hospital, automatically importing physical records, diagnostic results, etc. Thus, the platform can continuously collect and update the medical history information of the user so as to perform accurate health assessment.
In addition, when users participate in health examination every year or periodically, the medical institution can provide blood examination, urine examination, imaging examination and other examination data, and the users can upload the data to the health management platform through the electronic medical record system so as to further perfect own health files. Physical examination reports generally include detailed blood glucose, blood pressure, blood lipid, liver function, kidney function, etc., and these physical examination results can provide more detailed health status information for the user and also provide important basis for subsequent health risk assessment.
S202, performing intelligent evaluation on the health physical examination data by using a deep learning algorithm to obtain the health state of the user;
Through training and prediction of the deep learning model, the system can automatically extract key features from health physical examination data of the user, and calculate and analyze the relation among the features through a complex model so as to obtain an accurate health state assessment result. Specifically, the system firstly processes and normalizes various collected data (such as physiological indexes, physical examination results, medical history information and life habit data) so as to ensure that the input data of the model have consistency. The model then predicts from the training dataset, dividing the user's health status into several health levels, such as "healthy", "mild abnormal", "moderate abnormal", and "severe abnormal", etc. These assessment results will provide a scientific basis for subsequent health risk assessment and management.
The deep learning algorithm is used for intelligently evaluating the health physical examination data, and has important significance. Deep learning algorithms are able to capture implicit associations between potential health risks and data through the processing and pattern recognition of large-scale data. Traditional health assessment methods generally rely on manual analysis, lack sufficient depth and accuracy, and deep learning algorithms can perform self-learning and optimization through a multi-layer neural network, continuously improving the accuracy of assessment. Therefore, the deep learning algorithm can extract key information from complex and various health physical examination data, form more accurate and objective health state assessment, and provide scientific guidance for personalized health management of users.
Specifically, key index features associated with assessing the health state of the user may be extracted from the health physical examination data;
In this step, the system screens out the most critical characteristic indexes from the multidimensional data provided by the user according to the preset rules and model algorithms, and the indexes can effectively reflect the health condition of the user. For example, physiological indexes such as blood sugar, blood fat, weight, heart rate, etc., and life habit data such as smoking, drinking, exercise frequency, etc., are all selected as key features. These characteristics not only directly affect health status, but are also closely related to potential disease risk. Through feature extraction, the system can find the data with the most value for health evaluation in a large amount of information, so that the subsequent intelligent evaluation is more accurate and targeted.
The extraction of key index features from health physical examination data is an important step for ensuring the effectiveness of a subsequent deep learning model. Through feature extraction, the system can reject redundant or irrelevant data, and only retains key factors that have significant impact on health status assessment. The process is beneficial to reducing data noise and improving the calculation efficiency of the evaluation model, and meanwhile, the model can capture the health risk signal more accurately when facing complex health physical examination data, and finally, more reasonable and reliable health state prediction is provided.
In this step, the health examination data first needs to be preprocessed. Because of the variety of sources of data provided by users, which may include physiological data, medical history information, physical examination results, and lifestyle data from different devices, the preprocessing stage is important. The preprocessing step comprises denoising, missing value filling, outlier detection and normalization processing of the data. For example, if there is a lack of blood glucose level in the user, the system will fill the blank based on the user's historical data or other available data, and if some physiological data has significant outliers (such as heart rate far beyond normal range), the system will mark and remind the doctor or user to review. Through the series of steps, the system ensures the quality and consistency of data, and lays a foundation for subsequent feature extraction.
Next, feature selection is the core task at this stage. By using statistical methods and machine learning techniques, the system can extract key indicators that are closely related to health status from a large amount of data. For example, physiological indexes such as heart rate, blood pressure, blood sugar, weight, etc., and lifestyle habits such as smoking, drinking, exercise amount, etc., are important features for user health assessment. The system may employ correlation analysis, information gain, etc. to evaluate the contribution of each index to the health assessment. In this process, the system screens out those index features that can significantly affect health. For example, if there is an abnormality in the weight and blood glucose of the user, the system would consider these two indicators as key features, which should be incorporated into the subsequent assessment model.
Finally, to better assess health status, the system may also consider the dynamically changing nature of the physiological indicators. For example, the trend of weight fluctuation of the user over the past three months, daily fluctuation of blood pressure, etc., may be more important than a single blood pressure value. The system will extract these trending features based on time series analysis in order to capture possible health risks. For example, a user's body weight continues to rise for the past three months, which may be a potential diabetes risk signal. By extracting these dynamic changes, the system is able to more fully assess the health of the user.
And inputting the key index features into a pre-trained health state assessment model based on a deep learning algorithm, and outputting the health state of the user.
The pre-trained deep learning model is usually a multi-layer neural network, and can learn complex correlations between different features through training of a large amount of health physical examination data. At this step, the system will use the model to predict the health status of the user by entering key feature data. Health status is typically indicated by classification labels, such as "healthy", "mild abnormal", "moderate abnormal", "severe abnormal", and the like. The health state output by the model is used as the basis for subsequent recommendation and intervention of the health management system.
The extracted features are input into a deep learning model for evaluation, so that the change trend of each health index and the interaction among different health data can be comprehensively considered, and the health state evaluation result is more accurate and comprehensive. The traditional method may not be capable of processing complex, multidimensional data relationships, and the deep learning algorithm can automatically learn from complex data and discover implicit rules therein, thereby improving the accuracy of health assessment. By this step, the user can get a data-driven, personalized health assessment result based on which the system can provide tailored health management advice.
The system will input key features extracted from the health examination data into a deep learning model that has been trained in advance. The model is generally of a Deep Neural Network (DNN) architecture, is trained by a large number of marked health data, and can automatically learn a complex relationship between input data and health state output. In model training, the system may use health data for a large number of different users, including physiological metrics, physical examination results, and lifestyle data, as well as actual health status labels for each user (e.g., "healthy," "mild abnormal," "moderate abnormal," "severe abnormal"). Through a back propagation algorithm, the model gradually optimizes the weight of the model, and reduces the prediction error, so that the prediction accuracy of new data is improved.
Specifically, the system first vectorizes the extracted key features and inputs these features into a deep learning model. Each input characteristic (such as blood sugar, weight, exercise amount and the like) is multiplied by the weight of the model, and the health state predicted value is finally output through nonlinear transformation of a plurality of neural network layers. These predictions are converted to health status categories by an activation function (e.g., softmax or sigmoid function), and the model outputs labels that ultimately are "healthy" or "abnormal" and are further subdivided into mild, moderate, or severe anomalies, as the case may be.
To enhance the accuracy of the assessment, deep learning models typically incorporate various techniques, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), to extract features of time series data (e.g., blood pressure, weight trend). For some time sensitive indicators, such as blood glucose fluctuations and heart rate variability, RNNs can effectively capture the time dependence in the data, thereby improving the predictive power of the model. Thus, the model not only can analyze single data points, but also can know the health state of the user in the time dimension, and further improves the accuracy of prediction.
In another implementation, the deep learning model typically uses an efficient training framework, such as TensorFlow, pyTorch, to accelerate the computation by the GPU to increase training speed and accuracy. In the model training process, the system uses the marked historical health data set to cover various health states (such as health, mild abnormality and the like) and corresponding health indexes and life habit data. The data set in the training process comprises basic physiological data, physical examination results, living habits and other information of the user, and the health state is classified according to the evaluation labels of doctors. Through multiple iterative training, the model gradually adjusts its weight to minimize errors and improve the accuracy of the predictions of health.
When the data of a new user is input, the system automatically inputs the extracted key feature vectors into the trained model to predict. The model will output corresponding health status, such as "healthy", "mild abnormal", etc., based on the entered characteristic data. The output result can be provided to the user with a probability value, for example, the probability of health is 90%, and the probability of mild abnormality is 10%. The method can not only rapidly and accurately evaluate the health condition of the user, but also provide powerful support for subsequent personalized health management, and help the user to know the health condition of the user in time and take proper intervention measures.
S203, calculating the comprehensive health risk index of the user according to the health physical examination data;
In this step, the system calculates an integrated Health Risk Index (HRI) for measuring the current health risk level of the user by performing an integrated analysis of the user's health check data. The core of calculating the index is to carry out weighted evaluation according to factors of multiple aspects such as physiological indexes, living habits, medical history information and the like of the user. Each health indicator is assigned a weight coefficient to reflect the importance of the indicator in the user's health condition, while also taking into account the difference between each health indicator and the preset ideal value (i.e., the deviation of the current value of the health indicator from the ideal value). In addition, the system analyzes the sensitivity, trend and influence of external risk factors of the indexes, and further refines the risk assessment. Through the process, the comprehensive health risk index can provide a scientific and quantitative health risk assessment result for the user, help identify potential health problems and provide basis for subsequent health management decisions.
Calculating the comprehensive health risk index provides a comprehensive and quantitative health risk assessment for the user. By comprehensively considering the current conditions, trends and external risk factors of various health indexes, the comprehensive health risk index can help a user to determine which health indexes have higher risks, so that the user is guided to take proper health intervention measures. The index can be calculated to provide feedback in real time during health examination, and can also provide dynamic guidance for long-term health management by monitoring index change. For example, for chronically ill patients with hypertension, diabetes, etc., that are monitored for a long period of time, HRI can reflect the trend of their health risk in time and provide a reference for adjusting treatment regimens and lifestyle. Meanwhile, the comprehensive health risk index is used as an integrity score, and a plurality of health dimensions can be comprehensively evaluated, so that a user has an omnibearing knowledge on the health condition of the user, and further, the health awareness and the management capability are improved.
Specifically, the calculation formula of the comprehensive health risk index is as follows:
Wherein the said For an integrated health risk index, theA weight coefficient for the ith health index, theAn absolute value of a difference between the current value of the ith health index and a preset ideal value, theReflecting the relationship between the index and the health risk for the sensitivity coefficient of the ith health index, saidA trend coefficient of the ith index, representing the trend of the change of the health index, saidThe risk index is the j-th external risk factor, n is the number of health indexes, and m is the number of external risk factors.
In the design of the integrated Health Risk Index (HRI), the formula adopts a multiple weighting mode so as to comprehensively consider the influences of various health indexes, sensitivity, trend coefficients and external risk factors. The main purpose of the design is to quantify and integrate a plurality of factors affecting health risks through analysis of each health index. The various parameters in the HRI formula help the system fully reflect the importance of each health indicator in health risk and how to interact with other factors. Through the design, the HRI can reflect the actual health risk condition of the user more accurately, and scientific basis is provided for health management and risk early warning.
HRI (integrated health risk index) HRI is the final calculated health risk index representing the current integrated health risk level of the user. It can provide a comprehensive health risk assessment result by weighting the impact of various health indicators. The higher the HRI value, the greater the health risk, and the more health interventions the user may need to take. The value of the HRI is determined by the combination of the parameters in the formula. The weight coefficient, the health index difference value, the sensitivity coefficient, the trend coefficient and the like are optimized through data analysis and expert experience so as to ensure that the health risk of the user can be accurately reflected.
W_i (weight coefficient of the ith health indicator), which is used to represent the importance of each health indicator in the integrated health risk assessment. Different health indicators may have different degrees of impact on health risk among different individuals, and therefore, each indicator is assigned a different weight to ensure that important indicators can occupy a greater proportion in the calculation. For example, blood glucose, blood pressure, etc. have a large impact on the risk of cardiovascular disease, and thus their weighting coefficients may be high. The weighting coefficients are typically determined by means of data analysis or expert investigation. In practical application, the influence of different indexes can be evaluated by using a machine learning method (such as regression analysis), so that reasonable weights are given. A common way of determining is to make a statistical calculation based on the historical health record of the dataset and the correlation of disease occurrence.
I_i (absolute value of difference between the current value of the I-th health index and the preset ideal value) is the difference between the current value of the I-th health index and the preset ideal value, and can quantify the deviation between the health index and the ideal value, and the larger the deviation is, the larger the health risk is. For example, an ideal value range of blood glucose is that fasting blood glucose is within a normal value range, and if the current value is far higher than the ideal value, i_i is larger, which means that blood glucose is abnormal and health risk is higher. The ideal value is typically set according to medical standards or health guidelines. For example, a range of normal physiological indicators recommended by national or international health organizations. The current value is obtained by real-time detection or user health physical examination data.
S_i (sensitivity coefficient of the ith health index) S_i represents the sensitivity of the ith health index to health risk. The sensitivity of different health indicators to disease may be different, and smaller fluctuations in certain indicator changes may mean greater health risks. For example, slight changes in body weight may have a greater impact on cardiovascular health, while fluctuations in some non-physiological indicators may be less sensitive. The sensitivity coefficient may be determined by regression analysis or expert estimation of a large number of health data, in combination with actual case data, to determine which changes in the index are more sensitive to health risk. The sensitivity coefficient is set according to the influence degree of different physiological indexes on health risks in health research and medical literature.
T_i (trend coefficient of the ith health index) T_i represents the change trend of the ith health index, and is used for reflecting the change rate and direction of the index. Some indicators may exhibit a tendency to change rapidly, such as a drastic increase or decrease in body weight, generally implying a higher health risk. The trend coefficient considers the trend of the health index over time and helps to evaluate the potential risk of an index. The trend coefficient is typically determined by the rate of change of the index over a period of time. For example, the trend of change is calculated by time-series changes of the same health index (e.g., body weight, blood glucose) in the health physical examination data a plurality of times in succession. The trend of the index change can also be predicted by using a time series analysis method.
R_j (risk index of jth external risk factor) external risk factors including, but not limited to, environmental pollution, occupational disease, genetic background, stress, etc. R_j represents a potential threat to health by external factors, and the user's health risk assessment is further adjusted by the influence of external factors. For example, high risk work in certain professional environments (such as prolonged exposure to hazardous chemicals) may significantly increase health risk, and r_j will give a higher risk index. The determination of external risk factors typically depends on the experience of medical professionals and a large amount of epidemiological study data. For example, the weight of risk factors such as genetic diseases, environmental pollution, etc. can be determined by investigation and analysis and related medical literature.
S204, generating a health report of the user based on the health state, the comprehensive health risk index and the health physical examination data so as to realize management of the health physical examination data.
In this step, the system will integrate the user's health status, integrated Health Risk Index (HRI), and health physical examination data to generate detailed health reports. The generation of the health report is not just a simple summary of the data, but rather a personalized, scientific health report is formed based on the deep analysis of the health status and risk assessment. Firstly, the system classifies the health state of the user according to a preset template and rule, combines the health state with the HRI value and determines the current health risk level of the user. For example, if the user's HRI value is high, which may mean that there is a risk of cardiovascular disease, this is highlighted in the report and corresponding health management advice is provided. Second, the health report presents the current status of various physiological indexes, such as blood sugar, blood pressure, etc., showing whether these indexes are in normal range and the difference from the ideal health value. Finally, the health report also incorporates external risk factors (e.g., family history, environmental factors, etc.), evaluates potential factors that may affect health, and incorporates the user's lifestyle to make viable health management recommendations. For example, if the user's eating habits are irregular, the report may suggest adjustments to the eating structure and increase the amount of exercise to reduce the associated health risk.
By generating the health report based on the health status, the health risk index and the health physical examination data, comprehensive and accurate health analysis can be provided for the user, and the user is helped to better understand the health status of the user. The health report can be used as a reference for health management, can be used as a core component of personal health files, continuously tracks health changes, and adjusts a health management scheme according to the change trend. The individuation and the data driving characteristics of the report can effectively support the user to make more scientific health decisions. For example, when a user's health report shows abnormal fluctuations in certain key indicators, the user can learn about the potential risk in time and take corresponding health interventions. At the same time, the generation of the health report helps the health professionals and health management personnel to better evaluate the health condition of the user, and customize personalized treatment or intervention schemes, thereby realizing more efficient health management.
In a specific implementation, the generation process of the health report depends on a health management platform arranged in the system. Firstly, the platform integrates all key indexes extracted from the physical examination data of the user, and calculates the risk level of each index. For example, by calculating the differences between the blood sugar, the body weight and the blood pressure of the user and the ideal values, it is judged whether or not there is abnormal fluctuation in these indices. If an index fluctuates abnormally, the system can give priority to the importance of the index and determine the presentation mode of the index in the report according to the sensitivity coefficient of the index in the health risk. And then, the system evaluates the overall health state of the user according to the health physical examination data of the user and the generated comprehensive health risk index, and comprehensively judges by combining external risk factors. Finally, when generating a report, the system can clearly display the dynamic variation trend of each physiological index and the health risk by using the visualization tools (such as charts, graphs and the like). In addition, the system can automatically generate personalized health management suggestions according to the health condition of the user, such as adding exercise, adjusting diet, checking regularly and the like, so as to help the user improve the health condition.
In particular, the health report includes, but is not limited to, the health status of the user, the integrated health risk index, dynamic changes in various physiological indicators, disease risk assessment, and health management advice.
Firstly, the health status is the core part of the report, and the current health status of the user is determined by comprehensively considering various physiological indexes and health data. For example, if blood glucose levels are high and weight is increasing, the report may indicate that the user may be pre-diabetic and provide a corresponding alert. The integrated Health Risk Index (HRI) is a key indicator that demonstrates the overall health risk of a user by a quantified number. If the HRI value is higher, the report may indicate that there is a greater health risk and a more aggressive health management policy may need to be adopted. Dynamic changes in various physiological indicators are presented through time trend graphs, such as trend of changes in blood pressure, weight and cholesterol, so as to help users understand the influence of the changes of the indicators on health. Meanwhile, the disease risk assessment section predicts a possible disease risk in the future, such as heart disease, diabetes, etc., based on the history data and the current health index. Finally, the health report may provide targeted health management advice based on the analysis results, such as improving dietary structure, increasing exercise amount, regular physical examination, etc., to help the user reduce health risks and maintain physical health.
The health report is rich in content and highly personalized, can help users to know the health condition of the users deeply, and provides scientific and operable health management advice for the users. Through the health report, the user can not only know the current health state, but also clearly see the change trend of each physiological index and potential health risk, thereby effectively preventing and intervening. The disease risk assessment part helps the user to identify possible disease risks in advance through deep analysis of the user health data, and adopts corresponding preventive measures, thereby being beneficial to realizing the accuracy of health management. In addition, the generation of the health management advice is based on the actual data of the user, ensuring the pertinence and the effectiveness of the advice. For example, if the report suggests that the user is continually increasing in weight and is at a high risk of diabetes, the recommendation would recommend a reduction in high-sugar food intake and an increase in the amount of movement, helping the user to effectively control weight and blood glucose levels. These personalized advice can significantly improve the user's health management awareness, helping him to better manage his own health.
The health examination method comprises the steps of obtaining health examination data of a user, wherein the health examination data comprise physiological indexes, medical history information, physical examination results and life habit data of the user, intelligently evaluating the health examination data by means of a deep learning algorithm to obtain the health state of the user, calculating comprehensive health risk indexes of the user according to the health examination data, and generating a health report of the user based on the health state, the comprehensive health risk indexes and the health examination data to achieve management of the health examination data, so that comprehensive and accurate health risk evaluation can be achieved, and a more accurate and personalized health management scheme is provided.
Still another embodiment of the present invention provides a health examination data management system, see fig. 3, which may include:
the acquiring module 301 is configured to acquire health physical examination data of a user, where the health physical examination data includes a physiological index, medical history information, a physical examination result, and life habit data of the user;
The evaluation module 302 is configured to intelligently evaluate the health physical examination data by using a deep learning algorithm to obtain a health state of the user;
A calculating module 303, configured to calculate a comprehensive health risk index of the user according to the health physical examination data;
The generating module 304 is configured to generate a health report of the user based on the health status, the comprehensive health risk index and the health physical examination data, so as to implement management of the health physical examination data.
The health examination method comprises the steps of obtaining health examination data of a user, wherein the health examination data comprise physiological indexes, medical history information, physical examination results and life habit data of the user, intelligently evaluating the health examination data by means of a deep learning algorithm to obtain the health state of the user, calculating comprehensive health risk indexes of the user according to the health examination data, and generating a health report of the user based on the health state, the comprehensive health risk indexes and the health examination data to achieve management of the health examination data, so that comprehensive and accurate health risk evaluation can be achieved, and a more accurate and personalized health management scheme is provided.
The embodiment of the invention also provides a storage medium, in which a computer program is stored, wherein the computer program is configured to perform the steps of any of the method embodiments described above when run.
Specifically, in the present embodiment, the above-described storage medium may be configured to store a computer program for executing the steps of:
s201, acquiring health physical examination data of a user, wherein the health physical examination data comprise physiological indexes, medical history information, physical examination results and life habit data of the user;
S202, performing intelligent evaluation on the health physical examination data by using a deep learning algorithm to obtain the health state of the user;
S203, calculating the comprehensive health risk index of the user according to the health physical examination data;
S204, generating a health report of the user based on the health state, the comprehensive health risk index and the health physical examination data so as to realize management of the health physical examination data.
The health examination method comprises the steps of obtaining health examination data of a user, wherein the health examination data comprise physiological indexes, medical history information, physical examination results and life habit data of the user, intelligently evaluating the health examination data by means of a deep learning algorithm to obtain the health state of the user, calculating comprehensive health risk indexes of the user according to the health examination data, and generating a health report of the user based on the health state, the comprehensive health risk indexes and the health examination data to achieve management of the health examination data, so that comprehensive and accurate health risk evaluation can be achieved, and a more accurate and personalized health management scheme is provided.
The present invention also provides an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Specifically, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
Specifically, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
s201, acquiring health physical examination data of a user, wherein the health physical examination data comprise physiological indexes, medical history information, physical examination results and life habit data of the user;
S202, performing intelligent evaluation on the health physical examination data by using a deep learning algorithm to obtain the health state of the user;
S203, calculating the comprehensive health risk index of the user according to the health physical examination data;
S204, generating a health report of the user based on the health state, the comprehensive health risk index and the health physical examination data so as to realize management of the health physical examination data.
The health examination method comprises the steps of obtaining health examination data of a user, wherein the health examination data comprise physiological indexes, medical history information, physical examination results and life habit data of the user, intelligently evaluating the health examination data by means of a deep learning algorithm to obtain the health state of the user, calculating comprehensive health risk indexes of the user according to the health examination data, and generating a health report of the user based on the health state, the comprehensive health risk indexes and the health examination data to achieve management of the health examination data, so that comprehensive and accurate health risk evaluation can be achieved, and a more accurate and personalized health management scheme is provided.
The construction, features and effects of the present invention have been described in detail with reference to the embodiments shown in the drawings, but the above description is only a preferred embodiment of the present invention, but the present invention is not limited to the embodiments shown in the drawings, all changes, or modifications to the teachings of the invention, which fall within the meaning and range of equivalents are intended to be embraced therein, are intended to be embraced therein.
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