Method and system for judging chronic diseases in the elderly based on data analysis

Abstract

The present invention discloses a method and system for judging elderly chronic diseases based on data analysis, which belongs to the technical field of elderly chronic diseases. The method includes: collecting real-time data of elderly patients and determining characteristic data of elderly patients after processing; training and optimizing a model for judging elderly chronic diseases, analyzing and judging the characteristic data of elderly patients, predicting the risk of elderly chronic diseases, determining the results of judgment of elderly chronic diseases, and performing early warning and health management on elderly patients. The present invention solves the problem that the existing system cannot judge elderly chronic diseases and timely manage health, resulting in poor diagnosis and management effects of elderly chronic diseases. The present invention constructs a model for judging elderly chronic diseases that predicts the risk of elderly chronic diseases and judges elderly chronic diseases, analyzes and judges the characteristic data of elderly patients, predicts the risk of elderly chronic diseases, and determines the results of judgment of elderly chronic diseases. It can effectively judge elderly chronic diseases and timely manage health and treat them, thereby improving the diagnosis and management effects of elderly chronic diseases.

Description

Technical Field

[0001] The present invention relates to the technical field of chronic diseases in the elderly, and specifically to a method and system for determining chronic diseases in the elderly based on data analysis. Background Art

[0002] The prevalence of multiple chronic diseases among the elderly has increased year by year. Common diseases include hypertension, diabetes, cardiovascular and cerebrovascular diseases, etc. Therefore, the prevention and management of chronic diseases in the elderly has become an important public health issue.

[0003] Chinese patent publication number CN117393131A discloses a management method and system for follow-up nursing services for elderly chronic diseases. The method includes: a medical service agency pushes an invitation for follow-up nursing services for elderly chronic diseases to the elderly, divides the nursing staff of the medical service agency, and assigns them to the elderly who need different nursing services, then performs follow-up nursing, enters the data of each follow-up nursing visit into the system, submits the information in the system and the follow-up nursing service evaluation form to the elderly who need nursing services in different ways, analyzes the follow-up nursing service evaluation form, determines the quality of the follow-up nursing service and optimizes it, thereby improving the timeliness of patient treatment and recovery. However, this patent has the following defects:

[0004] Existing technologies cannot effectively diagnose chronic diseases in the elderly and provide timely health management and treatment, resulting in poor diagnosis and management of chronic diseases in the elderly. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for judging chronic diseases in the elderly based on data analysis, which can effectively judge chronic diseases in the elderly and provide timely health management and treatment, improve the diagnosis and management effect of chronic diseases in the elderly, and solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] Methods for judging chronic diseases in the elderly based on data analysis include:

[0008] Collect real-time data of elderly patients and determine their characteristic data after processing;

[0009] Train and optimize the elderly chronic disease judgment model, analyze and judge the characteristic data of elderly patients, predict the risk of elderly chronic diseases, determine the judgment results of elderly chronic diseases, and provide early warning and health management for elderly patients.

[0010] Preferably, real-time data of elderly patients is collected, including:

[0011] Real-time monitoring and collection of elderly patients' diet, exercise and sleep conditions based on intelligent data collection equipment to obtain elderly patients' life data;

[0012] Based on intelligent data collection equipment, the body temperature, heart rate, blood pressure, blood oxygen saturation, blood sugar, blood lipids and body mass index of elderly patients are monitored and collected in real time to obtain physiological data of elderly patients;

[0013] Determine the real-time data of elderly patients based on their life data and physiological data.

[0014] Preferably, the body temperature, heart rate, blood pressure and blood oxygen saturation of elderly patients are monitored and collected in real time based on intelligent data collection equipment, including:

[0015] Real-time collection of body temperature data of elderly patients;

[0016] Comparing the body temperature data of the elderly patient with a standard value of healthy body temperature, wherein the standard value of healthy body temperature is 37.3 degrees Celsius;

[0017] When the body temperature data of the elderly patient exceeds the healthy body temperature standard value, the change growth rate of the elderly patient's heart rate, blood pressure and blood oxygen saturation is collected;

[0018] comparing the change growth rate of the elderly patient's heart rate, blood pressure and blood oxygen saturation with the growth rate of the elderly patient's body temperature data;

[0019] Whether to adjust the data collection frequency is determined based on the quantitative relationship between the change growth rate of the elderly patient's heart rate, blood pressure and blood oxygen saturation and the growth rate of the elderly patient's body temperature data.

[0020] Preferably, determining whether to adjust the data collection frequency based on the quantitative relationship between the growth rate of changes in the elderly patient's heart rate, blood pressure, and blood oxygen saturation and the growth rate of the elderly patient's body temperature data includes:

[0021] The change growth ratios of the heart rate, blood pressure and blood oxygen saturation of the elderly patient are subjected to data scale compression processing, so that the change growth ratios of the heart rate, blood pressure and blood oxygen saturation of the elderly patient are projected to a value of (0, 0.5];

[0022] The data values ​​of the change growth ratios of the heart rate, blood pressure, and blood oxygen saturation after data scale compression processing are obtained by the following formula:

[0023]

[0024] Where R represents the data value of heart rate, blood pressure and blood oxygen saturation after data scale compression processing; x represents the data value of heart rate, blood pressure and blood oxygen saturation before data scale compression processing;

[0025] Comparing the data values ​​of the change growth ratios of the heart rate, blood pressure and blood oxygen saturation after data scale compression processing with the growth ratio of the body temperature data of the elderly patient;

[0026] Extract the data whose corresponding data scale compression processing value among the change growth ratio of heart rate, blood pressure and blood oxygen saturation is not lower than the growth ratio of the body temperature data of the elderly patient as the target data;

[0027] The data collection frequency of heart rate, blood pressure, and blood oxygen saturation is adjusted using the change growth rate of the target data combined with the growth rate of the body temperature data of the elderly patient. The adjusted data collection frequency of heart rate, blood pressure, and blood oxygen saturation is obtained by the following formula:

[0028]

[0029] Where F represents the data collection frequency of heart rate, blood pressure and blood oxygen saturation after adjustment; F0 represents the data collection frequency of heart rate, blood pressure and blood oxygen saturation before adjustment; T b represents the growth rate of the body temperature data of elderly patients; n represents the number of data whose data value after data scale compression is not less than the growth rate of the body temperature data of elderly patients; R i Represents the data value after data scale compression processing corresponding to the i-th target data; R p Indicates the average value of the data after data scale compression corresponding to heart rate, blood pressure, and blood oxygen saturation.

[0030] Preferably, the real-time data of elderly patients is processed, including:

[0031] Based on Pandas, real-time data of elderly patients is cleaned to identify duplicate values, missing values, and outliers in the data. Duplicate values, missing values, and outliers that are not useful for elderly chronic disease diagnosis are deleted, and missing values ​​and outliers that are useful for elderly chronic disease diagnosis are filled and corrected.

[0032] The real-time data of elderly patients are standardized based on Min-Max standardization, so that the real-time data of elderly patients are scaled to the interval of [0, 1], the dimension differences between the real-time data of elderly patients are removed, and the standardized real-time data of elderly patients are determined.

[0033] Preferably, processing the real-time data of elderly patients also includes:

[0034] Integrate the real-time data of elderly patients into a unified data view, verify the integrity of the integrated real-time data of elderly patients, and determine whether the integrated real-time data of elderly patients is missing. Once the data integrity verification is qualified, the integrated real-time data of elderly patients will be securely stored in the database;

[0035] Feature extraction is performed on the real-time data of elderly patients. Features related to the judgment of elderly chronic diseases are extracted from the real-time data of elderly patients to determine the characteristic data of elderly patients, including blood pressure fluctuation rate and blood sugar change trend.

[0036] Preferably, the elderly chronic disease judgment model is trained and optimized, including:

[0037] According to the needs of elderly chronic diseases, collect historical data of elderly patients, divide the historical data of elderly patients, and determine the training set and test set;

[0038] Based on deep learning technology, a training set is used to train the deep learning model, allowing the deep learning model to autonomously learn the judgment behavior of elderly chronic diseases and predict the risk of elderly chronic diseases, thereby determining the elderly chronic disease judgment model;

[0039] The performance of the elderly chronic disease diagnosis model was tested based on the test set. The accuracy, recall rate, and F1 score were used to determine whether the elderly chronic disease diagnosis model could achieve the expected results.

[0040] When the elderly chronic disease judgment model cannot achieve the expected effect, the parameters and structure of the elderly chronic disease judgment model are adjusted, and the optimal elderly chronic disease judgment model is determined after continuous iterative optimization.

[0041] Preferably, the characteristic data of elderly patients are analyzed and judged to predict the risk of chronic diseases in the elderly, including:

[0042] Obtain the optimal model for judging chronic diseases in the elderly and deploy it in the actual environment for judging chronic diseases in the elderly;

[0043] The characteristic data of elderly patients are input into the optimal model for judging elderly chronic diseases. The characteristic data of elderly patients are analyzed and judged according to the optimal model for judging elderly chronic diseases, the incidence risk of elderly chronic diseases is predicted, and the judgment results of elderly chronic diseases are determined.

[0044] Preferably, early warning and health management are carried out for elderly patients, including:

[0045] Based on the judgment results of elderly chronic diseases, timely warnings are given to elderly patients with high risks of developing chronic diseases, and characteristic data of elderly patients are analyzed to form chronic disease reports of elderly patients. Health management and treatment plans are formulated for elderly patients based on the chronic disease reports of elderly patients, and the health management and treatment status of elderly patients is monitored in real time. The health management and treatment plans are dynamically adjusted according to the real-time monitoring feedback of elderly patients.

[0046] According to another aspect of the present invention, a system for determining chronic diseases in the elderly based on data analysis is provided, which is used to implement the above-mentioned method for determining chronic diseases in the elderly based on data analysis. The system includes: a data acquisition module, a data processing module, a model building module, a chronic disease determination module, and an early warning management module;

[0047] The data acquisition module is configured to determine real-time data of the elderly patient based on the elderly patient's life data and physiological data;

[0048] The data processing module is configured to clean, standardize, integrate and extract features from the real-time data of elderly patients to determine the feature data of elderly patients;

[0049] The model building module is configured to build a geriatric chronic disease judgment model for predicting the risk of geriatric chronic diseases and judging geriatric chronic diseases;

[0050] The chronic disease judgment module is configured to analyze and judge the characteristic data of elderly patients according to the elderly chronic disease judgment model, predict the risk of elderly chronic diseases, and determine the elderly chronic disease judgment result;

[0051] The early warning management module is configured to provide timely early warning and health management for elderly patients with high risk of developing chronic diseases based on the judgment results of elderly chronic diseases.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention determines the real-time data of elderly patients based on their life data and physiological data, determines the characteristic data of elderly patients by cleaning, standardizing, integrating and extracting features from their real-time data, and constructs a chronic disease judgment model for the elderly that predicts the risk of chronic diseases in the elderly and judges chronic diseases in the elderly according to the needs of judging chronic diseases in the elderly. The characteristic data of elderly patients are analyzed and judged according to the chronic disease judgment model, the risk of chronic diseases in the elderly is predicted, the judgment results of chronic diseases in the elderly are determined, and timely early warning and health management are carried out for elderly patients with high risks of chronic diseases in the elderly. Chronic diseases in the elderly can be effectively judged and timely health management and treatment can be carried out for chronic diseases in the elderly, which can improve the diagnosis and management effect of chronic diseases in the elderly. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1This is a flow chart of the method for determining chronic diseases in the elderly based on data analysis of the present invention;

[0055] Figure 2 This is a module diagram of the data analysis-based elderly chronic disease judgment system of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] In order to solve the existing problem of inability to effectively judge chronic diseases in the elderly and timely health management and treatment, resulting in poor diagnosis and management of chronic diseases in the elderly, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0058] The elderly chronic disease judgment system based on data analysis includes: data acquisition module, data processing module, model building module, chronic disease judgment module and early warning management module;

[0059] The data acquisition module is configured to determine the real-time data of elderly patients based on their life data and physiological data; the data processing module is configured to clean, standardize, integrate and extract features from the real-time data of elderly patients to determine the characteristic data of elderly patients; the model construction module is configured to construct a geriatric chronic disease judgment model for predicting the risk of developing geriatric chronic diseases and judging geriatric chronic diseases; the chronic disease judgment module is configured to analyze and judge the characteristic data of elderly patients according to the geriatric chronic disease judgment model, predict the risk of developing geriatric chronic diseases, and determine the judgment results of geriatric chronic diseases; the early warning management module is configured to provide timely early warning and health management for elderly patients with high risk of developing geriatric chronic diseases according to the judgment results of geriatric chronic diseases.

[0060] Specifically, based on the life data and physiological data of elderly patients, the real-time data of elderly patients is determined. By cleaning, standardizing, integrating and extracting features from the real-time data of elderly patients, the characteristic data of elderly patients is determined. According to the needs of elderly chronic disease judgment, a chronic disease judgment model for the elderly is constructed to predict the risk of elderly chronic diseases and judge elderly chronic diseases. According to the chronic disease judgment model for the elderly, the characteristic data of elderly patients are analyzed and judged, the risk of elderly chronic diseases is predicted, the judgment results of elderly chronic diseases are determined, and timely early warning and health management are carried out for elderly patients with high risk of elderly chronic diseases. Effective judgment of elderly chronic diseases and timely health management and treatment can be carried out, which can improve the diagnosis and management effect of elderly chronic diseases.

[0061] In order to better demonstrate the elderly chronic disease judgment process based on data analysis, this embodiment now provides an elderly chronic disease judgment method based on data analysis, which is implemented using an elderly chronic disease judgment system based on data analysis, including: collecting real-time data of elderly patients and determining the characteristic data of elderly patients after processing; training and optimizing the elderly chronic disease judgment model, analyzing and judging the characteristic data of elderly patients, predicting the incidence risk of elderly chronic diseases, determining the elderly chronic disease judgment results, and providing early warning and health management for elderly patients.

[0062] In this embodiment, real-time data of elderly patients is collected, including:

[0063] Real-time monitoring and collection of elderly patients' diet, exercise and sleep conditions based on intelligent data collection equipment to obtain elderly patients' life data;

[0064] It should be noted that real-time monitoring and collection of diet, exercise and sleep conditions of elderly patients is an important means of chronic disease management and health intervention; by combining the Internet of Things, wearable devices and big data analysis technology, comprehensive collection and real-time monitoring of elderly people's health data can be achieved.

[0065] Specifically, use smart bowls, smart chopsticks and other devices to record the type, weight and intake time of food, analyze food composition and calories through image recognition technology, and monitor the eating behavior of elderly patients, such as the number of chewing times and swallowing frequency, through smart bracelets or watches.

[0066] Specifically, smart bracelets or watches can be used to monitor the number of steps, calorie consumption, exercise distance, etc. of elderly patients in real time; smart insoles can be used to monitor the gait, walking distance and pressure distribution of elderly patients to prevent falls; and infrared sensors or cameras can be used to monitor the indoor activities of elderly patients.

[0067] Specifically, the sleeping posture, breathing rate and heart rate of elderly patients are monitored through smart mattresses, the sleep duration, the ratio of deep sleep to light sleep, and the number of awakenings are recorded through smart bracelets or watches, and the temperature, humidity and light of the bedroom are monitored through environmental sensors to optimize the sleeping environment.

[0068] Based on intelligent data collection equipment, the body temperature, heart rate, blood pressure, blood oxygen saturation, blood sugar, blood lipids and body mass index of elderly patients are monitored and collected in real time to obtain physiological data of elderly patients;

[0069] It should be noted that real-time monitoring and collection of body temperature, heart rate, blood pressure, blood oxygen saturation, blood sugar, blood lipids and body mass index (BMI) of elderly patients is an important means of chronic disease management and health intervention. By combining the Internet of Things, wearable devices and big data analysis technology, comprehensive collection and real-time monitoring of these physiological indicators can be achieved.

[0070] Specifically, when monitoring body temperature, a smart thermometer is used to monitor the body temperature of elderly patients in real time, supporting continuous monitoring and abnormal warnings, and body temperature can also be measured regularly through a non-contact infrared thermometer.

[0071] Specifically, when monitoring heart rate, an optical sensor PPG is used to monitor heart rate in real time, supporting the monitoring of resting heart rate and exercise heart rate. A portable ECG device can also be used to monitor electrocardiogram signals.

[0072] Specifically, when monitoring blood pressure, use an upper arm or wrist electronic blood pressure monitor to measure blood pressure regularly. The data can be synchronized to the mobile app, and wearable devices can also be used for non-invasive continuous blood pressure monitoring.

[0073] Specifically, when monitoring blood oxygen saturation, an optical sensor PPG is used to monitor blood oxygen saturation in real time, and a portable finger clip oximeter can also be used to measure blood oxygen saturation regularly.

[0074] Specifically, when monitoring blood sugar, an implantable or patch-type CGM device is used to monitor blood sugar levels in real time, and a fingertip blood glucose meter can also be used to measure blood sugar regularly.

[0075] Specifically, when monitoring blood lipids, portable devices are used to regularly measure blood lipid indicators such as cholesterol and triglycerides.

[0076] Specifically, when monitoring body mass index (BMI), use a smart scale to measure weight and body fat percentage and automatically calculate BMI.

[0077] In this embodiment, the real-time data of the elderly patients is determined based on the elderly patients' life data and physiological data, providing data support for subsequent judgment of the elderly patients' chronic diseases.

[0078] Specifically, the body temperature, heart rate, blood pressure and blood oxygen saturation of elderly patients are monitored and collected in real time based on intelligent data collection equipment, including:

[0079] Real-time collection of body temperature data of elderly patients;

[0080] Comparing the body temperature data of the elderly patient with a standard value of healthy body temperature, wherein the standard value of healthy body temperature is 37.3 degrees Celsius;

[0081] When the body temperature data of the elderly patient exceeds the healthy body temperature standard value, the change growth rate of the elderly patient's heart rate, blood pressure and blood oxygen saturation is collected;

[0082] comparing the change growth rate of the elderly patient's heart rate, blood pressure and blood oxygen saturation with the growth rate of the elderly patient's body temperature data;

[0083] Whether to adjust the data collection frequency is determined based on the quantitative relationship between the change growth rate of the elderly patient's heart rate, blood pressure and blood oxygen saturation and the growth rate of the elderly patient's body temperature data.

[0084] The technical effect of the above-mentioned technical solution is that by collecting physiological data such as body temperature, heart rate, blood pressure, and blood oxygen saturation in real time from elderly patients, the acquired information accurately reflects the patient's current physical condition, avoiding information bias caused by untimely collection or missing data. Comparing body temperature data with the standard value of healthy body temperature, and further comparing the growth ratio of heart rate, blood pressure, and blood oxygen saturation to body temperature data, helps to more accurately judge the elderly patient's physical condition, providing precise data support for subsequent medical decision-making. Real-time collection of body temperature data, as well as timely collection of the growth ratio of other physiological data when the body temperature is abnormal, can quickly detect abnormal changes in the elderly patient's physical condition, providing early warning of potential health problems, allowing medical staff to take appropriate measures. By determining whether to adjust the data collection frequency based on the relationship between the growth ratios of various physiological data, the collection frequency can be matched to changes in the patient's condition. When the patient's physical condition changes significantly, the collection frequency can be increased to more closely monitor the disease progression; when the condition is stable, the collection frequency can be appropriately reduced. This ensures data validity while rationally utilizing resources and ensuring that dynamic changes in the disease are captured in a timely manner. Adjusting the data collection frequency based on changes in the patient's physiological data avoids unnecessary high-frequency data collection when the patient's condition is stable. This reduces resource usage, such as power consumption and storage resources, on the intelligent data collection device, extending its service life. Targeted data collection and frequency adjustment enhance the value of collected data, reduce the collection and processing of invalid data, and improve the efficiency of the entire medical monitoring system, allowing medical staff to more effectively focus on the patient's actual condition. By comprehensively considering multiple physiological indicators, such as body temperature, heart rate, blood pressure, and blood oxygen saturation, and their changing growth rates, the medical condition of elderly patients is assessed from multiple dimensions. Compared to monitoring a single indicator, this provides a more comprehensive and accurate picture of the patient's health status and improves the reliability of monitoring results. By comparing the quantitative relationships between various indicators to determine whether to adjust the data collection frequency, abnormal correlations between physiological indicators can be detected promptly, providing early warning of potential health issues and providing more reliable protection for the health of elderly patients.

[0085] Specifically, judging whether to adjust the data collection frequency based on the quantitative relationship between the growth rate of the elderly patient's heart rate, blood pressure, and blood oxygen saturation and the growth rate of the elderly patient's body temperature data includes:

[0086] The change growth ratios of the heart rate, blood pressure and blood oxygen saturation of the elderly patient are subjected to data scale compression processing, so that the change growth ratios of the heart rate, blood pressure and blood oxygen saturation of the elderly patient are projected to a value of (0, 0.5];

[0087] The data values ​​of the change growth ratios of the heart rate, blood pressure, and blood oxygen saturation after data scale compression processing are obtained by the following formula:

[0088]

[0089] Where R represents the data value of heart rate, blood pressure and blood oxygen saturation after data scale compression processing; x represents the data value of heart rate, blood pressure and blood oxygen saturation before data scale compression processing;

[0090] Comparing the data values ​​of the change growth ratios of the heart rate, blood pressure and blood oxygen saturation after data scale compression processing with the growth ratio of the body temperature data of the elderly patient;

[0091] Extract the data whose corresponding data scale compression processing value among the change growth ratio of heart rate, blood pressure and blood oxygen saturation is not lower than the growth ratio of the body temperature data of the elderly patient as the target data;

[0092] The data collection frequency of heart rate, blood pressure, and blood oxygen saturation is adjusted using the change growth rate of the target data combined with the growth rate of the body temperature data of the elderly patient. The adjusted data collection frequency of heart rate, blood pressure, and blood oxygen saturation is obtained by the following formula:

[0093]

[0094] Where F represents the data collection frequency of heart rate, blood pressure and blood oxygen saturation after adjustment; F0 represents the data collection frequency of heart rate, blood pressure and blood oxygen saturation before adjustment; T b represents the growth rate of the body temperature data of elderly patients; n represents the number of data whose data value after data scale compression is not less than the growth rate of the body temperature data of elderly patients; R i Represents the data value after data scale compression processing corresponding to the i-th target data; R p Indicates the average value of the data after data scale compression corresponding to heart rate, blood pressure, and blood oxygen saturation.

[0095] The technical effect of the above technical solution is as follows: by compressing the data scale of the change growth ratio of the heart rate, blood pressure and blood oxygen saturation of elderly patients and projecting them into the numerical interval (0, 0.5], these data can be compared and analyzed at the same scale, simplifying the complexity of the data. This processing method facilitates subsequent calculations and judgments, improves the efficiency of data processing, avoids the analysis difficulties caused by large differences in data scales, and enables the system to process and evaluate the changes in these physiological indicators more quickly. Data with a value not lower than the growth ratio of the elderly patients' body temperature data after data scale compression is extracted as the target data, clarifying the data range of focus. This screening method can focus on physiological indicator data that is closely related to body temperature changes and has a large degree of change, reducing unnecessary data interference, and further improving the pertinence and effectiveness of data processing.

[0096] The data collection frequency for heart rate, blood pressure, and blood oxygen saturation is adjusted using the target data growth rate combined with the elderly patient's temperature data growth rate, comprehensively considering the relationships between multiple physiological indicators. Compared to single-indicator analysis, this multi-indicator analysis provides a more comprehensive and accurate reflection of changes in the elderly patient's physical condition, enabling more precise adjustment of the data collection frequency. When changes in certain physiological indicators align with and are more significant in relation to temperature, increasing the data collection frequency allows for more timely and accurate monitoring of the patient's condition, improving monitoring accuracy. The above technical solution calculates the adjusted data collection frequency for heart rate, blood pressure, and blood oxygen saturation, providing a quantitative and scientific basis for frequency adjustment. The formula considers multiple factors, including the pre-adjustment collection frequency, the temperature data growth rate, the number of target data points, the corresponding value of each target data point, and the average value after data resizing. This makes frequency adjustment more rational and scientific, enabling dynamic optimization based on the patient's actual condition, further improving monitoring accuracy. Adjusting the data collection frequency based on changes in physiological indicator data avoids unnecessary high-frequency data collection when the patient's condition is stable, thereby conserving resources such as power and storage capacity of the intelligent data collection device. When a patient's condition is relatively stable and physiological indicators show minimal changes, the data collection frequency can be reduced. This ensures timely detection of changes in the condition, extending the device's lifespan, reducing resource waste, and improving resource utilization efficiency. By optimizing the data collection frequency, the collection equipment can more effectively allocate resources to monitor different physiological indicators. For physiological indicators with significant changes and those related to body temperature, the collection frequency is increased to ensure more detailed data; for indicators with less significant changes, the frequency is appropriately reduced, ensuring more efficient use of device resources and improving the performance and stability of the entire monitoring system.

[0097] In this embodiment, the real-time data of elderly patients is processed, including:

[0098] Based on Pandas, real-time data of elderly patients is cleaned to identify duplicate values, missing values, and outliers in the data. Duplicate values, missing values, and outliers that are not useful for elderly chronic disease diagnosis are deleted, and missing values ​​and outliers that are useful for elderly chronic disease diagnosis are filled and corrected.

[0099] Standardize the real-time data of elderly patients based on Min-Max standardization, scale the real-time data of elderly patients to the interval [0, 1], remove the dimension differences between the real-time data of elderly patients, and determine the standardized real-time data of elderly patients;

[0100] Integrate the real-time data of elderly patients into a unified data view, verify the integrity of the integrated real-time data of elderly patients, and determine whether the integrated real-time data of elderly patients is missing. Once the data integrity verification is qualified, the integrated real-time data of elderly patients will be securely stored in the database;

[0101] Feature extraction is performed on the real-time data of elderly patients. Features related to the judgment of elderly chronic diseases are extracted from the real-time data of elderly patients to determine the characteristic data of elderly patients, including blood pressure fluctuation rate and blood sugar change trend.

[0102] It should be noted that by cleaning, standardizing, integrating and extracting features from the real-time data of elderly patients, the characteristic data of elderly patients can be determined, which facilitates the subsequent use of the elderly chronic disease judgment model to analyze and judge the characteristic data of elderly patients, predict the risk of elderly chronic diseases, and determine the judgment results of elderly chronic diseases.

[0103] In this embodiment, training and optimizing the elderly chronic disease judgment model includes:

[0104] According to the needs of elderly chronic diseases, collect historical data of elderly patients, divide the historical data of elderly patients, and determine the training set and test set;

[0105] Based on deep learning technology, a training set is used to train the deep learning model, allowing the deep learning model to autonomously learn the judgment behavior of elderly chronic diseases and predict the risk of elderly chronic diseases, thereby determining the elderly chronic disease judgment model;

[0106] The performance of the elderly chronic disease diagnosis model was tested based on the test set. The accuracy, recall rate, and F1 score were used to determine whether the elderly chronic disease diagnosis model could achieve the expected results.

[0107] When the elderly chronic disease judgment model cannot achieve the expected effect, the parameters and structure of the elderly chronic disease judgment model are adjusted, and the optimal elderly chronic disease judgment model is determined after continuous iterative optimization.

[0108] In this embodiment, the characteristic data of elderly patients are analyzed and judged to predict the risk of chronic diseases in the elderly, including:

[0109] Obtain the optimal model for judging chronic diseases in the elderly and deploy it in the actual environment for judging chronic diseases in the elderly;

[0110] The characteristic data of elderly patients are input into the optimal model for judging elderly chronic diseases. The characteristic data of elderly patients are analyzed and judged according to the optimal model for judging elderly chronic diseases, the incidence risk of elderly chronic diseases is predicted, and the judgment results of elderly chronic diseases are determined.

[0111] In this embodiment, early warning and health management for elderly patients include:

[0112] Based on the judgment results of elderly chronic diseases, timely warnings are given to elderly patients with high risks of developing chronic diseases, and characteristic data of elderly patients are analyzed to form chronic disease reports of elderly patients. Health management and treatment plans are formulated for elderly patients based on the chronic disease reports of elderly patients, and the health management and treatment status of elderly patients is monitored in real time. The health management and treatment plans are dynamically adjusted according to the real-time monitoring feedback of elderly patients.

[0113] In summary, by collecting the life data and physiological data of elderly patients, the real-time data of elderly patients is determined. By cleaning, standardizing, integrating and extracting features of the real-time data of elderly patients, the characteristic data of elderly patients is determined. According to the needs of elderly chronic disease judgment, a chronic disease judgment model for the elderly is constructed to predict the risk of elderly chronic diseases and judge elderly chronic diseases. According to the chronic disease judgment model, the characteristic data of elderly patients are analyzed and judged to predict the risk of elderly chronic diseases, determine the judgment results of elderly chronic diseases, and provide timely early warning and health management for elderly patients with high risk of chronic diseases. It can effectively judge elderly chronic diseases and provide timely health management and treatment, which can improve the diagnosis and management effect of elderly chronic diseases. It can be applied in hospital and clinic scenarios, community health management scenarios and personal health management scenarios. When used in hospital and clinic scenarios, it assists doctors in early screening and diagnosis of chronic diseases, which can improve the diagnosis and treatment efficiency and reduce the misdiagnosis rate. When used in community health management scenarios, it provides regular health assessments and disease risk predictions for elderly patients, and supports community doctors in health intervention and follow-up. When used in personal health management scenarios, it monitors health data in real time through mobile apps or wearable devices and provides personalized health advice and early warnings.

[0114] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0115] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for judging chronic diseases in the elderly based on data analysis, characterized in that: include: Collect real-time data of elderly patients and determine their characteristic data after processing; Train and optimize the elderly chronic disease diagnosis model, analyze and judge the characteristic data of elderly patients, predict the risk of elderly chronic diseases, determine the diagnosis results of elderly chronic diseases, and provide early warning and health management for elderly patients; Collect real-time data from elderly patients, including: Real-time monitoring and collection of body temperature, heart rate, blood pressure and blood oxygen saturation of elderly patients based on intelligent data collection equipment, including: Real-time collection of body temperature data of elderly patients; Comparing the body temperature data of the elderly patient with a standard value of healthy body temperature, wherein the standard value of healthy body temperature is 37.3 degrees Celsius; When the body temperature data of the elderly patient exceeds the healthy body temperature standard value, the change growth rate of the elderly patient's heart rate, blood pressure and blood oxygen saturation is collected; comparing the change growth rate of the elderly patient's heart rate, blood pressure and blood oxygen saturation with the growth rate of the elderly patient's body temperature data; Determining whether to adjust the data collection frequency according to the quantitative relationship between the change growth rate of the elderly patient's heart rate, blood pressure, and blood oxygen saturation and the growth rate of the elderly patient's body temperature data includes: The change growth ratios of the heart rate, blood pressure and blood oxygen saturation of the elderly patient are subjected to data scale compression processing, so that the change growth ratios of the heart rate, blood pressure and blood oxygen saturation of the elderly patient are projected to a value of (0, 0.5]; Comparing the data values ​​of the heart rate, blood pressure and blood oxygen saturation change growth ratios after data scale compression processing with the growth ratio of the body temperature data of the elderly patient; Extract the data whose corresponding data scale compression processing value among the change growth ratio of heart rate, blood pressure and blood oxygen saturation is not lower than the growth ratio of the body temperature data of the elderly patient as the target data; The data collection frequency of heart rate, blood pressure, and blood oxygen saturation is adjusted using the change growth rate of the target data combined with the growth rate of the body temperature data of the elderly patient. The adjusted data collection frequency of heart rate, blood pressure, and blood oxygen saturation is obtained by the following formula: Where F represents the data collection frequency of heart rate, blood pressure and blood oxygen saturation after adjustment; F0 represents the data collection frequency of heart rate, blood pressure and blood oxygen saturation before adjustment; T b represents the growth rate of the body temperature data of elderly patients; n represents the number of data whose data value after data scale compression is not less than the growth rate of the body temperature data of elderly patients; R i Represents the data value after data scale compression processing corresponding to the i-th target data; R p Indicates the average value of the data after data scale compression corresponding to heart rate, blood pressure, and blood oxygen saturation.

2. The method for determining chronic diseases in the elderly based on data analysis according to claim 1, characterized in that: Collect real-time data from elderly patients, including: Real-time monitoring and collection of elderly patients' diet, exercise and sleep conditions based on intelligent data collection equipment to obtain elderly patients' life data; Based on intelligent data collection equipment, the body temperature, heart rate, blood pressure, blood oxygen saturation, blood sugar, blood lipids and body mass index of elderly patients are monitored and collected in real time to obtain physiological data of elderly patients; Determine the real-time data of elderly patients based on their life data and physiological data.

3. The method for determining chronic diseases in the elderly based on data analysis according to claim 2, characterized in that: Processing of real-time data of elderly patients, including: Based on Pandas, real-time data of elderly patients is cleaned to identify duplicate values, missing values, and outliers in the data. Duplicate values, missing values, and outliers that are not useful for elderly chronic disease diagnosis are deleted, and missing values ​​and outliers that are useful for elderly chronic disease diagnosis are filled and corrected. The real-time data of elderly patients are standardized based on Min-Max standardization, so that the real-time data of elderly patients are scaled to the interval of [0, 1], the dimension differences between the real-time data of elderly patients are removed, and the standardized real-time data of elderly patients are determined.

4. The method for determining chronic diseases in the elderly based on data analysis according to claim 3, characterized in that: Processing of real-time data of elderly patients also includes: Integrate the real-time data of elderly patients into a unified data view, verify the integrity of the integrated real-time data of elderly patients, and determine whether the integrated real-time data of elderly patients is missing. Once the data integrity verification is qualified, the integrated real-time data of elderly patients will be securely stored in the database; Feature extraction is performed on the real-time data of elderly patients. Features related to the judgment of elderly chronic diseases are extracted from the real-time data of elderly patients to determine the characteristic data of elderly patients, including blood pressure fluctuation rate and blood sugar change trend.

5. The method for determining chronic diseases in the elderly based on data analysis according to claim 4, characterized in that: Training and optimizing the elderly chronic disease diagnosis model, including: According to the needs of elderly chronic diseases, collect historical data of elderly patients, divide the historical data of elderly patients, and determine the training set and test set; Based on deep learning technology, a training set is used to train the deep learning model, allowing the deep learning model to autonomously learn the judgment behavior of elderly chronic diseases and predict the risk of elderly chronic diseases, thereby determining the elderly chronic disease judgment model; The performance of the elderly chronic disease diagnosis model was tested based on the test set. The accuracy, recall rate, and F1 score were used to determine whether the elderly chronic disease diagnosis model could achieve the expected results. When the elderly chronic disease judgment model cannot achieve the expected effect, the parameters and structure of the elderly chronic disease judgment model are adjusted, and the optimal elderly chronic disease judgment model is determined after continuous iterative optimization.

6. The method for determining chronic diseases in the elderly based on data analysis according to claim 5, characterized in that: Analyze and judge the characteristic data of elderly patients to predict the risk of chronic diseases in the elderly, including: Obtain the optimal model for judging chronic diseases in the elderly and deploy it in the actual environment for judging chronic diseases in the elderly; The characteristic data of elderly patients are input into the optimal model for judging elderly chronic diseases. The characteristic data of elderly patients are analyzed and judged according to the optimal model for judging elderly chronic diseases, the incidence risk of elderly chronic diseases is predicted, and the judgment results of elderly chronic diseases are determined.

7. The method for determining chronic diseases in the elderly based on data analysis according to claim 6, characterized in that: Provide early warning and health management for elderly patients, including: Based on the judgment results of elderly chronic diseases, timely warnings are given to elderly patients with high risks of developing chronic diseases, and characteristic data of elderly patients are analyzed to form chronic disease reports of elderly patients. Health management and treatment plans are formulated for elderly patients based on the chronic disease reports of elderly patients, and the health management and treatment status of elderly patients is monitored in real time. The health management and treatment plans are dynamically adjusted according to the real-time monitoring feedback of elderly patients.

8. A system for judging chronic diseases in the elderly based on data analysis, used to implement the method for judging chronic diseases in the elderly based on data analysis according to claim 7, characterized in that: The system includes: a data acquisition module, a data processing module, a model building module, a chronic disease judgment module and an early warning management module; The data acquisition module is configured to determine real-time data of the elderly patient based on the elderly patient's life data and physiological data; The data processing module is configured to clean, standardize, integrate and extract features from the real-time data of elderly patients to determine the feature data of elderly patients; The model building module is configured to build a geriatric chronic disease judgment model for predicting the risk of geriatric chronic diseases and judging geriatric chronic diseases; The chronic disease judgment module is configured to analyze and judge the characteristic data of elderly patients according to the elderly chronic disease judgment model, predict the risk of elderly chronic diseases, and determine the elderly chronic disease judgment result; The early warning management module is configured to provide timely early warning and health management for elderly patients with high risk of developing chronic diseases based on the judgment results of elderly chronic diseases.

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