Physical examination data management method and system

Abstract

The invention discloses a health physical examination data management method and system, and the method comprises the steps: obtaining the health physical examination data of a user, and the health physical examination data comprises the physiological indexes, medical history information, physical examination results and living habit data of the user; performing intelligent evaluation on the health examination data by using a deep learning algorithm to obtain a health state of the user; calculating a comprehensive health risk index of the user according to the health examination data; and based on the health state, the comprehensive health risk index and the health examination data, generating a health report of the user to realize management of the health examination data. According to 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

[0001] The present invention belongs to the technical field of data management, and in particular to a health examination data management method and system. Background Art

[0002] With the continuous advancement of modern medical technology, people's attention to health has gradually increased, and health check-ups have become a regular means of health management for many individuals and families. Health check-ups can not only help to detect potential health problems early, but also provide an important basis for subsequent health management through systematic data collection and analysis. Traditional health check-ups mainly include the measurement of individual physiological indicators, review of medical history, analysis of physical examination results, and inquiries about living habits. However, the existing health check-up data management methods have certain limitations, and cannot fully tap the potential of health data, and cannot achieve dynamic monitoring of health conditions and personalized recommendations. Summary of the invention

[0003] The purpose of the present invention is to provide a health examination data management method and system to address the deficiencies in the prior art, to achieve comprehensive and accurate health risk assessment, and to provide a more precise and personalized health management solution.

[0004] An embodiment of the present application provides a method for managing health examination data, the method comprising: Obtaining the user's health examination data, wherein the health examination data includes the user's physiological indicators, medical history information, physical examination results, and living habits data; Using a deep learning algorithm to intelligently evaluate the health examination data to obtain the health status of the user; Calculating the comprehensive health risk index of the user based on the health examination data; Based on the health status, the comprehensive health risk index and the health examination data, a health report of the user is generated to achieve management of the health examination data.

[0005] Optionally, the using a deep learning algorithm to intelligently evaluate the health examination data to obtain the health status of the user includes: Extracting key indicator features associated with assessing the user's health status from the health examination data; The key indicator features are input into a pre-trained health status assessment model based on a deep learning algorithm to output the health status of the user.

[0006] Optionally, the calculation formula of the comprehensive health risk index is: Among them, the is a comprehensive health risk index, is the weight coefficient of the i-th health indicator, is the absolute value of the difference between the current value of the i-th health indicator and the preset ideal value, is the sensitivity coefficient of the ith health indicator, reflecting the relationship between the indicator and health risk. is the trend coefficient of the i-th indicator, indicating the trend of the change of the health indicator. is the risk index of the jth external risk factor, n is the number of health indicators, and m is the number of external risk factors.

[0007] Optionally, the health report includes the user's health status, comprehensive health risk index, dynamic changes in various physiological indicators, disease risk assessment and health management recommendations.

[0008] Another embodiment of the present application provides a health examination data management system, the system comprising: An acquisition module is used to acquire the user's health examination data, wherein the health examination data includes the user's physiological indicators, medical history information, physical examination results, and living habits data; An evaluation module, used to use a deep learning algorithm to intelligently evaluate the health examination data to obtain the health status of the user; A calculation module, used to calculate the comprehensive health risk index of the user according to the health examination data; A generation module is used to generate a health report of the user based on the health status, the comprehensive health risk index and the health examination data to achieve the management of the health examination data.

[0009] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0010] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0011] Compared with the prior art, the present invention provides a health examination data management method, which obtains the health examination data of a user, wherein the health examination data includes the user's physiological indicators, medical history information, physical examination results, and living habit data; uses a deep learning algorithm to intelligently evaluate the health examination data to obtain the health status of the user; calculates the user's comprehensive health risk index based on the health examination data; and generates a health report of the user based on the health status, the comprehensive health risk index and the health examination data to realize the management of the health examination data, thereby achieving comprehensive and accurate health risk assessment and providing a more accurate and personalized health management plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A hardware structure block diagram of a computer terminal for a health examination data management method provided by an embodiment of the present invention; Figure 2 A flowchart of a health examination data management method provided by an embodiment of the present invention; Figure 3 A structural diagram of a health examination data management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0014] The embodiment of the present invention firstly provides a health examination data management method, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0015] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a health examination data management method provided by an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0016] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the health examination data management methods.

[0017] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0018] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any health examination data management method.

[0019] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0020] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0021] See also Figure 2 The embodiment of the present invention provides a health examination data management method, which may include the following steps: S201, obtaining health examination data of the user, wherein the health examination data includes physiological indicators, medical history information, physical examination results, and living habit data of the user; Obtaining the user's health examination data is the basis of the health management system. The health examination data includes four core information categories: physiological indicators, medical history information, physical examination results and lifestyle data. Physiological indicators refer to the basic health parameters of the body obtained through routine physical examinations, such as blood pressure, heart rate, body temperature, blood sugar, weight, height, etc., which can reflect the current health status of the individual. Medical history information includes the user's past disease records, family genetic history, previous surgical history, and known chronic diseases, etc., to help doctors assess potential health risks. Physical examination results refer to specific examination data obtained through physical examinations, such as blood tests, urine tests, imaging tests (such as X-rays, CT scan results), etc., to help identify whether there are certain potential health problems or diseases. Lifestyle data includes the user's eating habits, exercise, sleep quality, smoking and drinking, etc., reflecting the impact of lifestyle on health. These data are collected in a variety of ways, such as through smart devices, health management platforms, personal information entered by users, or doctor's diagnosis records.

[0022] By comprehensively collecting users' health checkup data, sufficient basic information can be provided for subsequent health assessment and management. Comprehensive analysis of various types of data not only helps to accurately assess the user's current health status, but also provides the necessary parameters for calculating health risks. Changes in physiological indicators can directly reflect an individual's health trends, while medical history information provides a historical basis for analyzing health risks, physical examination results help identify potential diseases, and living habit data reveals the relationship between a user's lifestyle and health. By combining these data, the health management system can conduct a multi-dimensional and comprehensive assessment of the user's health status, thereby providing personalized and accurate health advice, and ultimately achieving intelligent management of health examination data.

[0023] In order to achieve comprehensive management of health examination data, it is first necessary to obtain the user's health examination data through various means. Specifically, these data mainly include physiological indicators, medical history information, physical examination results and living habits data. The first step is to collect data through smart devices and health management systems. For example, the smart watch or bracelet worn by the user will record physiological indicators such as heart rate, number of steps, sleep quality, blood oxygen saturation, etc. in real time, and synchronize updates with the mobile phone or health management platform via Bluetooth or Wi-Fi. The user's weight, height, body fat percentage and other information can be collected through a smart scale and automatically uploaded to the health management system. These physiological data can accurately reflect the user's daily health status and provide an effective basis for subsequent analysis.

[0024] At the same time, medical history information needs to be filled in manually by users, imported by medical institutions, or updated through online health platforms. For example, when registering a health management account, users need to fill in their personal health records, including known chronic diseases, family medical history, and past major disease records. If users have undergone examinations at certain medical institutions, these data can be connected to the hospital's electronic health record (EHR) system to automatically import physical examination records, diagnosis results, etc. In this way, the platform can continuously collect and update users' medical history information for accurate health assessments.

[0025] In addition, when users participate in health check-ups every year or regularly, medical institutions will provide blood tests, urine tests, imaging tests and other physical examination data. Users can upload these data to the health management platform through the electronic medical record system to further improve their health records. Physical examination reports usually include detailed blood sugar, blood pressure, blood lipids, liver function, kidney function and other items. These physical examination results can provide users with more detailed health information and provide an important basis for subsequent health risk assessment.

[0026] S202, using a deep learning algorithm to intelligently evaluate the health examination data to obtain the health status of the user; Through the training and prediction of deep learning models, the system can automatically extract key features from the user's health checkup data, and analyze the relationship between these features through complex model calculations to obtain accurate health status assessment results. Specifically, the system will first process and normalize the various types of collected data (such as physiological indicators, physical examination results, medical history information, and lifestyle data) to ensure the consistency of the model's input data. Next, the model will make predictions based on the training data set and divide the user's health status into several health levels, such as "healthy", "mildly abnormal", "moderately abnormal", and "severely abnormal". These assessment results will provide a scientific basis for subsequent health risk assessment and management.

[0027] The use of deep learning algorithms to intelligently evaluate health examination data is of great significance. Deep learning algorithms can capture potential health risks and implicit associations between data through processing and pattern recognition of large-scale data. Traditional health assessment methods usually rely on manual analysis and lack sufficient depth and accuracy, while deep learning algorithms can self-learn and optimize through multi-layer neural networks to continuously improve the accuracy of assessments. Therefore, deep learning algorithms can extract key information from complex and diverse health examination data, form a more accurate and objective health status assessment, and provide scientific guidance for users' personalized health management.

[0028] Specifically, key indicator features associated with evaluating the user's health status may be extracted from the health examination data; In this step, the system will select the most critical feature indicators from the multidimensional data provided by the user according to the preset rules and model algorithms. These indicators can effectively reflect the user's health status. For example, physiological indicators such as blood sugar, blood lipids, weight, heart rate, and living habits such as smoking, drinking, and exercise frequency will be selected as key features. These features not only directly affect the health status, but are also closely related to potential disease risks. Through feature extraction, the system can find the most valuable data for health assessment in a large amount of information, making subsequent intelligent assessments more accurate and targeted.

[0029] Extracting key indicator features from health examination data is an important step to ensure the effectiveness of subsequent deep learning models. Through feature extraction, the system can eliminate redundant or irrelevant data and retain only those key factors that have a significant impact on health status assessment. This process helps reduce data noise and improve the computational efficiency of the evaluation model. At the same time, it enables the model to more accurately capture health risk signals when faced with complex health examination data, and ultimately provide more reasonable and reliable health status predictions.

[0030] In this step, the health checkup data must first be preprocessed. Since the data provided by users comes from various sources, including physiological data from different devices, medical history information, physical examination results, and living habits data, the preprocessing stage is very important. The preprocessing steps include denoising, missing value filling, outlier detection, and normalization of the data. For example, if a user's blood sugar value is missing, the system will fill in the gap based on the user's historical data or other available data; if some physiological data has obvious abnormal values ​​(such as heart rate far beyond the normal range), the system will mark and remind the doctor or user to review. Through this series of steps, the system ensures the quality and consistency of the data, laying the foundation for subsequent feature extraction.

[0031] Next, feature selection is the core task of 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 indicators such as heart rate, blood pressure, blood sugar, weight, and lifestyle habits such as smoking, drinking, and exercise are all important features for user health assessment. The system may use methods such as correlation analysis and information gain to evaluate the contribution of each indicator to health assessment. In this process, the system will screen out those indicator features that can significantly affect the health status. For example, if the user's weight and blood sugar are abnormal, the system will consider these two indicators to be key features and should be included in the subsequent evaluation model.

[0032] Finally, in order to better assess health status, the system can also consider the dynamic changes in physiological indicators. For example, the user's weight fluctuation trend over the past three months, the daily fluctuation of blood pressure, etc., may be more important than a single blood pressure value. The system will extract these trend characteristics based on time series analysis in order to capture possible health risks. For example, a user's weight has continued to increase over the past three months, which may be a potential risk signal for diabetes. By extracting these dynamic changes, the system can more comprehensively assess the user's health status.

[0033] The key indicator features are input into a pre-trained health status assessment model based on a deep learning algorithm to output the health status of the user.

[0034] This pre-trained deep learning model is usually a multi-layer neural network. After being trained with a large amount of health examination data, it can learn the complex correlations between different features. In this step, the system will use the model to predict the user's health status by inputting key feature data. The health status is usually represented by classification labels, such as "healthy", "mildly abnormal", "moderately abnormal", "severely abnormal", etc. The health status output by the model will serve as the basis for subsequent recommendations and interventions in the health management system.

[0035] Inputting the extracted features into the deep learning model for evaluation can comprehensively consider the changing trends of various health indicators and the mutual influence between different health data, making the health status assessment results more accurate and comprehensive. Traditional methods may not be able to handle complex, multi-dimensional data relationships, while deep learning algorithms can automatically learn from complex data and discover implicit rules, thereby improving the accuracy of health assessment. Through this step, users can get a data-driven, personalized health assessment result, and the system can provide tailored health management recommendations based on this result.

[0036] The system will input the key features extracted from the health checkup data into a pre-trained deep learning model. This model generally adopts a deep neural network (DNN) architecture and is trained with a large amount of labeled health data. It can automatically learn the complex relationship between input data and health status output. When training the model, the system will use a large amount of health data from different users, including physiological indicators, physical examination results, and lifestyle data, as well as the actual health status label of each user (such as "healthy", "mild abnormality", "moderate abnormality", "severe abnormality"). Through the back-propagation algorithm, the model will gradually optimize its weights, reduce prediction errors, and thus improve the accuracy of predictions for new data.

[0037] Specifically, the system first vectorizes the extracted key features and inputs them into the deep learning model. Each input feature (such as blood sugar, weight, exercise volume, etc.) is multiplied by the model's weight and undergoes nonlinear transformations in multiple neural network layers to ultimately output a predicted health status value. These predicted values ​​are converted into health status categories through activation functions (such as Softmax or sigmoid functions), and the model outputs a final label of "healthy" or "abnormal", which is further subdivided into mild abnormality, moderate abnormality, or severe abnormality depending on the specific situation.

[0038] In order to enhance the accuracy of evaluation, deep learning models usually combine multiple technologies, such as convolutional neural networks (CNN) or recurrent neural networks (RNN), to extract the characteristics of time-series data (such as blood pressure and weight change trends). For some time-sensitive indicators, such as blood sugar fluctuations and heart rate changes, RNN can effectively capture the time correlation in the data, thereby improving the model's predictive ability. In this way, the model can not only analyze a single data point, but also understand the user's health status from a time dimension, thereby improving the accuracy of the prediction.

[0039] In another specific implementation, deep learning models usually use efficient training frameworks such as TensorFlow, PyTorch, etc., and use GPU accelerated computing to improve training speed and accuracy. During the model training process, the system uses annotated historical health data sets, covering various health states (such as healthy, mildly abnormal, etc.) and corresponding health indicators and lifestyle data. The data set in the training process will include the user's basic physiological data, physical examination results, lifestyle information, and classify the health status according to the doctor's evaluation labels. Through multiple iterations of training, the model will gradually adjust its weights to minimize errors and improve the accuracy of health status prediction.

[0040] When a new user's data is input, the system will automatically input the extracted key feature vectors into the trained model for prediction. The model will output the corresponding health status based on the input feature data, such as "healthy", "mildly abnormal", etc. The output results can also be provided to users with probability values, such as a 90% probability of health and a 10% probability of mild abnormality. This method can not only quickly and accurately assess the user's health status, but also provide strong support for subsequent personalized health management, helping users to understand their health status in a timely manner and take appropriate intervention measures.

[0041] S203, calculating the comprehensive health risk index of the user according to the health examination data; In this step, by comprehensively analyzing the user's health examination data, the system will calculate a comprehensive health risk index (HRI) to measure the user's current health risk level. The core of calculating this index is to perform a weighted assessment based on multiple factors such as the user's physiological indicators, living habits, and medical history information. Each health indicator is assigned a weight coefficient to reflect the importance of the indicator in the user's health status. At the same time, the difference between each health indicator and the preset ideal value (that is, the deviation between the current value of the health indicator and the ideal value) must also be considered. In addition, the system will analyze the sensitivity, trend, and impact of external risk factors of these indicators to further refine the risk assessment. Through this process, the comprehensive health risk index can provide users with a scientific and quantitative health risk assessment result, help identify potential health problems, and provide a basis for subsequent health management decisions.

[0042] Calculating the comprehensive health risk index provides users with a comprehensive and quantitative health risk assessment. By comprehensively considering the current status, trends and external risk factors of multiple health indicators, the comprehensive health risk index can help users identify which health indicators are at higher risk, thereby guiding users to take appropriate health intervention measures. The calculation of this index can not only provide real-time feedback during health examinations, but also provide dynamic guidance for long-term health management by monitoring indicator changes. For example, for patients with chronic diseases such as hypertension and diabetes who are monitored for a long time, HRI can promptly reflect the changing trends of their health risks and provide a reference for adjusting treatment plans and lifestyles. At the same time, as a holistic score, the comprehensive health risk index can comprehensively evaluate multiple health dimensions, allowing users to have a comprehensive understanding of their own health status, thereby improving health awareness and management capabilities.

[0043] Specifically, the calculation formula of the comprehensive health risk index is: Among them, the is a comprehensive health risk index, is the weight coefficient of the i-th health indicator, is the absolute value of the difference between the current value of the i-th health indicator and the preset ideal value, is the sensitivity coefficient of the ith health indicator, reflecting the relationship between the indicator and health risk. is the trend coefficient of the i-th indicator, indicating the trend of the change of the health indicator. is the risk index of the jth external risk factor, n is the number of health indicators, and m is the number of external risk factors.

[0044] In the design of the comprehensive health risk index (HRI), the formula adopts a multiple weighting method to comprehensively consider the influence of various health indicators, sensitivity, trend coefficients and external risk factors. The main purpose of its design is to quantify and integrate multiple factors that affect health risks through the analysis of each health indicator. The various parameters in the HRI formula help the system fully reflect the importance of each health indicator in health risks and how it interacts with other factors. Through this design, HRI can more accurately reflect the user's actual health risk status and provide a scientific basis for health management and risk warning.

[0045] 1.HRI (Comprehensive Health Risk Index): HRI is the final calculated health risk index, which indicates the user's current comprehensive health risk level. 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 user may need to take more health intervention measures. The value of HRI is determined by the combined effect of various parameters in the formula. The weight coefficient, health indicator difference, sensitivity coefficient and trend coefficient are optimized through data analysis and expert experience to ensure that the user's health risk can be accurately reflected.

[0046] 2. w_i (weight coefficient of the i-th health indicator): This parameter is used to indicate the importance of each health indicator in the comprehensive health risk assessment. Different health indicators may have different impacts on health risks in different individuals. Therefore, different weights are assigned to each indicator to ensure that important indicators can occupy a larger proportion in the calculation. For example, blood sugar, blood pressure, etc. have a greater impact on the risk of cardiovascular disease, so their weight coefficients may be higher. The weight coefficient is usually determined by data analysis or expert surveys. In practical applications, machine learning methods (such as regression analysis) can be used to evaluate the influence of different indicators and then give reasonable weights. A common way to determine is to perform statistical calculations based on the correlation between historical health records and disease occurrence in the data set.

[0047] 3.I_i (the absolute value of the difference between the current value of the i-th health indicator and the preset ideal value): I_i represents the difference between the current value of the i-th health indicator and the preset ideal value. It can quantify the deviation between the health indicator and the ideal value. The larger the deviation, the greater the health risk. For example, the ideal range of blood sugar is that fasting blood sugar is within the normal range. If the current value is much higher than the ideal value, I_i is larger, indicating that blood sugar is abnormal and the health risk is higher. The ideal value is usually set according to medical standards or health guidelines. For example, the normal physiological indicator range recommended by national or international health organizations. The current value is obtained through real-time detection or user health examination data.

[0048] 4. S_i (sensitivity coefficient of the i-th health indicator): S_i represents the sensitivity of the i-th health indicator to health risks. Different health indicators may have different sensitivities to diseases, and smaller fluctuations in some indicators may mean greater health risks. For example, a slight change in weight may have a greater impact on cardiovascular health, while fluctuations in some non-physiological indicators may be less sensitive. The sensitivity coefficient can be determined through regression analysis or expert estimation of a large amount of health data, combined with actual case data to determine which indicator changes are more sensitive to health risks. The setting of the sensitivity coefficient should be based on the degree of impact of different physiological indicators on health risks in health research and medical literature.

[0049] 5. T_i (trend coefficient of the i-th health indicator): T_i represents the changing trend of the i-th health indicator, which is used to reflect the rate and direction of change of the indicator. Some indicators may show a trend of rapid change, such as a sharp increase or decrease in weight, which usually means a higher health risk. The trend coefficient takes into account the changing trend of health indicators over time, which helps to assess the potential risk of an indicator. The trend coefficient is usually determined by the rate of change of the indicator over a period of time. For example, the change trend is calculated by the time series changes of the same health indicator (such as weight, blood sugar) in multiple consecutive health examination data. Time series analysis methods can also be used to predict the trend of indicator changes.

[0050] 6. R_j (risk index of the jth external risk factor): External risk factors include but are not limited to environmental pollution, occupational diseases, genetic background, stress, etc. R_j represents the potential threat to health from external factors, and the user's health risk assessment is further adjusted through the influence of external factors. For example, high-risk work in certain occupational environments (such as long-term exposure to harmful chemicals) may significantly increase health risks, and R_j will be assigned a higher risk index. The determination of external risk factors usually relies on the experience of medical experts and a large amount of epidemiological research data. For example, the weights of risk factors such as genetic diseases and environmental pollution can be determined through survey analysis and relevant medical literature.

[0051] S204, generating a health report of the user based on the health status, the comprehensive health risk index and the health examination data to achieve management of the health examination data.

[0052] In this step, the system will generate a detailed health report based on the user's health status, comprehensive health risk index (HRI) and health examination data. The generation of a health report is not just a simple summary of data, but a personalized and scientific health report based on in-depth analysis and risk assessment of health status. First, the system will classify the user's health status according to pre-set templates and rules, and combine it with the HRI value to clarify the user's current health risk level. For example, if the user's HRI value is high, it may mean that there is a risk of cardiovascular disease. This will be highlighted in the report and corresponding health management suggestions will be provided. Secondly, the health report will present the current status of various physiological indicators, such as blood sugar, blood pressure and other indicators, showing whether these indicators are within the normal range and the difference from the ideal health value. Finally, the health report will also combine external risk factors (such as family medical history, environmental factors, etc.) to evaluate potential factors that may affect health, and combine the user's living habits to put forward practical health management suggestions. For example, if the user's eating habits are irregular, the report may suggest adjusting the diet structure and increasing exercise to reduce related health risks.

[0053] By generating health reports based on health status, health risk index, and health examination data, users can be provided with comprehensive and accurate health analysis to help them better understand their own health status. This health report can not only serve as a reference for health management, but also as a core component of personal health records, continuously tracking health changes and adjusting health management plans according to changing trends. The personalized and data-driven nature of the report can effectively support users in making more scientific health decisions. For example, when a user's health report shows abnormal fluctuations in certain key indicators, the user can understand the potential risks in a timely manner and take corresponding health intervention measures. At the same time, the generation of health reports helps medical professionals and health managers better assess the user's health status and customize personalized treatment or intervention plans, thereby achieving more efficient health management.

[0054] In the specific implementation, the generation process of the health report will rely on the health management platform built into the system. First, the platform will integrate all the key indicators extracted from the user's health examination data and calculate the risk level of each indicator. For example, by calculating the difference between the user's blood sugar, weight and blood pressure and the ideal value, it is determined whether these indicators have abnormal fluctuations. If an indicator fluctuates abnormally, the system will give priority to the importance of the indicator and determine how to present it in the report based on its sensitivity coefficient in health risks. Next, the system will evaluate the user's overall health status based on the user's health examination data and the generated comprehensive health risk index, and make a comprehensive judgment based on external risk factors. Finally, when generating a report, the system will use visualization tools (such as charts, curves, etc.) to clearly display the dynamic change trends of various physiological indicators and the level of health risks. In addition, the system will automatically generate personalized health management suggestions based on the user's health status, such as increasing exercise, adjusting diet, regular check-ups, etc., to help users improve their health status.

[0055] Specifically, the health report includes but is not limited to the user's health status, comprehensive health risk index, dynamic changes in various physiological indicators, disease risk assessment and health management recommendations.

[0056] First of all, health status is the core part of the report, which determines the user's current health status by comprehensively considering various physiological indicators and health data. For example, if the blood sugar level is high and the weight increases, the report will point out that the user may be in the pre-diabetes stage and give corresponding warnings. The comprehensive health risk index (HRI) is a key indicator that shows the user's overall health risk through quantitative values. If the HRI value is high, the report will point out that there is a greater health risk and a more active health management strategy may be needed. The dynamic changes of various physiological indicators will be presented through time trend graphs, such as the trend of changes in blood pressure, weight and cholesterol, to help users understand the impact of changes in these indicators on health. At the same time, the disease risk assessment section predicts possible future disease risks, such as heart disease, diabetes, etc., based on historical data and current health indicators. Finally, the health report will provide targeted health management suggestions based on the analysis results, such as improving diet structure, increasing exercise, regular physical examinations, etc., to help users reduce health risks and stay healthy.

[0057] The health report is rich in content and highly personalized, which can help users gain an in-depth understanding of their health status and provide them with scientific and actionable health management suggestions. Through the health report, users can not only understand their current health status, but also clearly see the changing trends of various physiological indicators and potential health risks, so as to effectively prevent and intervene. The disease risk assessment part helps users identify possible disease risks in advance and take corresponding preventive measures through in-depth analysis of user health data, which helps to achieve precise health management. In addition, the generation of health management suggestions is based on the user's actual data, ensuring the pertinence and effectiveness of the suggestions. For example, if the report indicates that the user's weight continues to increase and there is a higher risk of diabetes, the suggestion will recommend reducing the intake of high-sugar foods and increasing exercise to help users effectively control their weight and blood sugar levels. These personalized suggestions can significantly improve users' awareness of health management and help them better control their own health.

[0058] It can be seen that the user's health check-up data is obtained, wherein the health check-up data includes the user's physiological indicators, medical history information, physical examination results, and living habit data; the health check-up data is intelligently evaluated using a deep learning algorithm to obtain the user's health status; based on the health check-up data, the user's comprehensive health risk index is calculated; based on the health status, the comprehensive health risk index and the health check-up data, a health report of the user is generated to realize the management of the health check-up data, thereby achieving comprehensive and accurate health risk assessment and providing more accurate and personalized health management solutions.

[0059] Another embodiment of the present invention provides a health examination data management system, see Figure 3 , the system may include: The acquisition module 301 is used to acquire the user's health examination data, wherein the health examination data includes the user's physiological indicators, medical history information, physical examination results, and living habits data; An evaluation module 302 is used to perform intelligent evaluation on the health examination data using a deep learning algorithm to obtain the health status of the user; A calculation module 303 is used to calculate the comprehensive health risk index of the user according to the health examination data; The generation module 304 is used to generate a health report of the user based on the health status, the comprehensive health risk index and the health examination data to achieve the management of the health examination data.

[0060] It can be seen that the user's health check-up data is obtained, wherein the health check-up data includes the user's physiological indicators, medical history information, physical examination results, and living habit data; the health check-up data is intelligently evaluated using a deep learning algorithm to obtain the user's health status; based on the health check-up data, the user's comprehensive health risk index is calculated; based on the health status, the comprehensive health risk index and the health check-up data, a health report of the user is generated to realize the management of the health check-up data, thereby achieving comprehensive and accurate health risk assessment and providing more accurate and personalized health management solutions.

[0061] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0062] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, obtaining health examination data of the user, wherein the health examination data includes physiological indicators, medical history information, physical examination results, and living habit data of the user; S202, using a deep learning algorithm to intelligently evaluate the health examination data to obtain the health status of the user; S203, calculating the comprehensive health risk index of the user according to the health examination data; S204, generating a health report of the user based on the health status, the comprehensive health risk index and the health examination data to achieve management of the health examination data.

[0063] It can be seen that the user's health check-up data is obtained, wherein the health check-up data includes the user's physiological indicators, medical history information, physical examination results, and living habit data; the health check-up data is intelligently evaluated using a deep learning algorithm to obtain the user's health status; based on the health check-up data, the user's comprehensive health risk index is calculated; based on the health status, the comprehensive health risk index and the health check-up data, a health report of the user is generated to realize the management of the health check-up data, thereby achieving comprehensive and accurate health risk assessment and providing more accurate and personalized health management solutions.

[0064] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0065] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0066] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, obtaining health examination data of the user, wherein the health examination data includes physiological indicators, medical history information, physical examination results, and living habit data of the user; S202, using a deep learning algorithm to intelligently evaluate the health examination data to obtain the health status of the user; S203, calculating the comprehensive health risk index of the user according to the health examination data; S204, generating a health report of the user based on the health status, the comprehensive health risk index and the health examination data to achieve management of the health examination data.

[0067] It can be seen that the user's health check-up data is obtained, wherein the health check-up data includes the user's physiological indicators, medical history information, physical examination results, and living habit data; the health check-up data is intelligently evaluated using a deep learning algorithm to obtain the user's health status; based on the health check-up data, the user's comprehensive health risk index is calculated; based on the health status, the comprehensive health risk index and the health check-up data, a health report of the user is generated to realize the management of the health check-up data, thereby achieving comprehensive and accurate health risk assessment and providing more accurate and personalized health management solutions.

[0068] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A health examination data management method, characterized in that: The method comprises: Obtaining the user's health examination data, wherein the health examination data includes the user's physiological indicators, medical history information, physical examination results, and living habits data; Using a deep learning algorithm to intelligently evaluate the health examination data to obtain the health status of the user; Calculating the comprehensive health risk index of the user according to the health examination data; Based on the health status, the comprehensive health risk index and the health examination data, a health report of the user is generated to achieve management of the health examination data.

2. The method according to claim 1, characterized in that The using of a deep learning algorithm to intelligently evaluate the health examination data to obtain the health status of the user includes: Extracting key indicator features associated with assessing the user's health status from the health examination data; The key indicator features are input into a pre-trained health status assessment model based on a deep learning algorithm to output the health status of the user.

3. The method according to claim 2, characterized in that The calculation formula of the comprehensive health risk index is: Among them, the is a comprehensive health risk index, is the weight coefficient of the i-th health indicator, is the absolute value of the difference between the current value of the i-th health indicator and the preset ideal value, is the sensitivity coefficient of the ith health indicator, reflecting the relationship between the indicator and health risk. is the trend coefficient of the i-th indicator, indicating the trend of the change of the health indicator. is the risk index of the jth external risk factor, n is the number of health indicators, and m is the number of external risk factors.

4. The method according to claim 3, characterized in that The health report includes the user's health status, comprehensive health risk index, dynamic changes in various physiological indicators, disease risk assessment and health management suggestions.

5. A health examination data management system, characterized in that: The system comprises: An acquisition module is used to acquire the user's health examination data, wherein the health examination data includes the user's physiological indicators, medical history information, physical examination results, and living habits data; An evaluation module, used to use a deep learning algorithm to intelligently evaluate the health examination data to obtain the health status of the user; A calculation module, used to calculate the comprehensive health risk index of the user according to the health examination data; A generation module is used to generate a health report of the user based on the health status, the comprehensive health risk index and the health examination data to achieve the management of the health examination data.

6. The system according to claim 5, characterized in that The evaluation module is specifically used for: Extracting key indicator features associated with assessing the user's health status from the health examination data; The key indicator features are input into a pre-trained health status assessment model based on a deep learning algorithm to output the health status of the user.

7. The system according to claim 6, characterized in that The calculation formula of the comprehensive health risk index is: Among them, the is a comprehensive health risk index, is the weight coefficient of the i-th health indicator, is the absolute value of the difference between the current value of the i-th health indicator and the preset ideal value, is the sensitivity coefficient of the ith health indicator, reflecting the relationship between the indicator and health risk. is the trend coefficient of the i-th indicator, indicating the trend of the change of the health indicator. is the risk index of the jth external risk factor, n is the number of health indicators, and m is the number of external risk factors.

8. The system according to claim 7, characterized in that The health report includes the user's health status, comprehensive health risk index, dynamic changes in various physiological indicators, disease risk assessment and health management suggestions.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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