Medical health assessment system and method for old people

Abstract

The invention discloses a medical health assessment system and method for old people, and relates to the technical field of internet medical treatment. Comprising a data acquisition and preprocessing module, an initial risk assessment module, a multi-dimensional health assessment module, a dynamic monitoring and feedback adjustment module, a comprehensive decision support module, a health management plan generation module, a system verification and iteration module, an old people interface and interaction module and a safety and privacy protection module. Through a multi-dimensional health assessment model integrating physiological health, psychological health, lifestyle, social participation and environmental factors, the overall health condition of the old people is comprehensively assessed, not only a single health index is concerned, meanwhile, an intelligent clinical decision rule engine is introduced, and the health condition of the old people can be comprehensively assessed according to preset clinical rules and health data of the old people. Decision logic is automatically executed, and health suggestions and treatment schemes are generated.

Description

Technical Field

[0001] The present invention relates to the field of Internet medical technology, and specifically to a medical health assessment system and method for the elderly. Background Art

[0002] Existing medical health assessment methods for the elderly are comprehensive and multi-dimensional processes, aiming to comprehensively evaluate the health status of the elderly and provide health management suggestions. It helps to identify the health problems of the elderly, formulate targeted health management plans, thereby improving the quality of life of the elderly and contributing to the realization of healthy aging. Through these assessments, the chronic diseases and comorbidities of the elderly can be managed more effectively, the overuse of medical resources can be reduced, and the satisfaction of medical services can be improved.

[0003] The existing technology only focuses on the assessment of physiological indicators, ignoring the multi-dimensional health impacts of the mental health, lifestyle, social participation and environmental factors of the elderly, often resulting in a one-sided understanding of the health status of the elderly and being unable to provide comprehensive health management suggestions. Moreover, there is a lack of continuous dynamic monitoring, and health assessments are only carried out at specific time points, unable to respond to changes in the health status of the elderly, resulting in the lag of health management measures. Therefore, we propose a medical health assessment system and method for the elderly to solve the problems raised above. Summary of the Invention

[0004] The purpose of the present invention is to provide a medical health assessment system and method for the elderly to solve one of the problems such as single-dimensional assessment and lack of dynamic assessment in the current market proposed in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A medical health assessment system for the elderly, comprising: a data collection and preprocessing module, an initial risk assessment module, a multi-dimensional health assessment module, a dynamic monitoring and feedback adjustment module, a comprehensive decision support module, a health management plan generation module, a system verification and iteration module, an elderly interface and interaction module, a security and privacy protection module.

[0007] As a further optimized solution of the present invention, the initial risk assessment module includes an information collection unit, a lifestyle information collection unit, a disease history collection unit, a physical examination data collection unit, a scale assessment unit, a preliminary risk calculation unit, a risk level division unit, a risk report generation unit, a risk warning and intervention suggestion unit;

[0008] As a further optimized solution of the present invention, the multi-dimensional health assessment module includes a physiological function assessment unit, a psychological state assessment unit, a social participation assessment unit, a nutritional status assessment unit, a chronic disease management assessment unit, a drug use assessment unit, a functional ability assessment unit, a disease risk prediction unit, and a comprehensive assessment report generation unit;

[0009] As a further optimized solution of the present invention, the dynamic monitoring and feedback adjustment module includes a real-time health data monitoring unit, a data analysis and anomaly detection unit, a risk warning trigger unit, a feedback information generation unit, a health management plan adjustment unit, and a family and medical service coordination unit;

[0010] As a further optimized solution of the present invention, the dynamic monitoring and feedback adjustment module includes a data integration unit, a risk assessment and classification unit, a decision rule base management unit, a decision support algorithm unit, a result interpretation and presentation unit, and an intervention plan recommendation unit;

[0011] As a further optimized solution of the present invention, the comprehensive decision support module includes a risk scoring unit, a clinical decision rule engine, a recommendation generation unit, a decision execution monitoring unit, and a feedback loop unit;

[0012] A medical health assessment method for the elderly includes the following steps:

[0013] Step 1: Collect the age, gender, medical history, family medical history, and lifestyle information of the elderly, measure height, weight, blood pressure, and heart rate, evaluate the eating habits, exercise frequency, smoking and drinking status of the elderly, and conduct a preliminary analysis of the collected data to identify obvious health risks or problems;

[0014] Step 2: Conduct pulmonary function tests, cardiac function tests, and muscle strength tests through medical devices, use the MMSE and depression scales to evaluate cognitive function and emotional state, understand the social activity participation of the elderly to evaluate their social network, evaluate the nutritional status of the elderly through dietary surveys and blood tests, and evaluate the control situation and management effect of chronic diseases based on past medical history;

[0015] Step 3: Calculate a comprehensive health risk score for the elderly according to the assessment results, set specific health management goals based on the risk score and assessment results, formulate intervention measures to implement a health management plan for the health problems found in the assessment, and regularly monitor the effects and adjust the plan according to the feedback.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] The present invention comprehensively evaluates the overall health status of the elderly through a health assessment model integrating multiple dimensions of physiological health, mental health, lifestyle, social participation, and environmental factors, not only focusing on a single health indicator. At the same time, an intelligent clinical decision rule engine is introduced, which can automatically execute decision logic based on preset clinical rules and the health data of the elderly to generate health suggestions and treatment plans.

[0018] Through the dynamic monitoring function, the present invention can track the health changes of the elderly in real time, adjust the health management plan in a timely manner through an adaptive feedback mechanism, and automatically generate a customized health management plan according to the risk assessment results, clinical decision suggestions, and personal preferences of the elderly.

[0019] The above summary is only for the purpose of the specification and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a block diagram of the modules of the medical health assessment system for the elderly of the present invention;

[0021] Figure 2 It is a unit block diagram of the initial risk assessment module of the present invention;

[0022] Figure 3 It is a unit block diagram of the multi-dimensional health assessment module of the present invention;

[0023] Figure 4 It is a unit block diagram of the dynamic monitoring and feedback adjustment module of the present invention;

[0024] Figure 5 It is a flowchart of the medical health assessment method for the elderly of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0026] Embodiment 1

[0027] Please refer to Figures 1-4, A medical health assessment system for the elderly, including a data collection and preprocessing module, an initial risk assessment module, a multi-dimensional health assessment module, a dynamic monitoring and feedback adjustment module, a comprehensive decision support module, a health management plan generation module, a system verification and iteration module, an elderly interface and interaction module, and a security and privacy protection module.

[0028] The data collection and preprocessing module includes a data collection unit, a data cleaning unit, a data conversion unit, a data verification unit, a data integration unit, and a data storage unit;

[0029] Specifically, in the data collection unit, connect to the data source through the API endpoint according to the type and location of the data source, and run SQL queries to extract data.

[0030] In the data cleaning unit, load the original data set to identify and delete duplicate records, find missing values and select filling strategies, and detect and delete or replace outliers.

[0031] In the data conversion unit, apply format conversion to date and time fields and encoding conversion to categorical data according to the data fields to be converted.

[0032] In the data verification unit, define the data type and allowed value range of the fields, apply verification rules to each field, record the verification results, and mark any data that does not conform to the rules.

[0033] In the data integration unit, select the common keys and index fields between data sets, use merge operations to merge the data sets, and check the merged data sets.

[0034] In the data storage unit, create a database connection using MySQL, write the cleaned and converted data into a database table, and close the database connection.

[0035] The initial risk assessment module includes an information collection unit, a lifestyle information collection unit, a disease history collection unit, a physical examination data collection unit, a scale assessment unit, a preliminary risk calculation unit, a risk level classification unit, a risk report generation unit, and a risk warning and intervention advice unit;

[0036] An information collection unit for collecting the age, gender, marital status, education level, and occupational background of patients; a lifestyle information collection unit for recording the eating habits, exercise frequency, smoking and drinking habits, and sleep quality of patients; a medical history collection unit for collecting the past medical history, family medical history, surgical history, and drug allergy history of patients; a physical examination data collection unit for recording the height, weight, blood pressure, heart rate, and blood glucose level of patients; a scale assessment unit for evaluating the cognitive function, activities of daily living, and depressive symptoms of patients through the Mini-Mental State Examination (MMSE) and the Activities of Daily Living Scale (ADL); a preliminary risk calculation unit for calculating a patient-specific health risk score based on the collected data using a preset risk assessment algorithm; a risk level classification unit for classifying patients into different risk levels based on the risk calculation results; a risk report generation unit for generating assessment results; a risk warning and intervention recommendation unit for providing warning information for patients with a high-risk assessment result and giving preliminary intervention recommendations according to the risk type.

[0037] Specifically, in the information collection unit, a data entry interface is launched to prompt the elderly to enter their age, gender, marital status, education level, and occupational background information. Format and range verification are performed on each data item entered by the elderly. When all data items pass the verification, the data is stored in the database; when a data item fails the verification, an error message is returned and the elderly are required to re-enter.

[0038] In the lifestyle information collection unit, the elderly select or enter their eating habits, exercise frequency, smoking and drinking habits, and sleep quality information, and convert the selection or input of the elderly into quantifiable scores, that is, convert the number of weekly exercise hours into an exercise frequency score, and store the quantified lifestyle information in the database.

[0039] The medical history collection unit extracts the medical history records of the elderly through the electronic medical record system interface or the elderly manually enter medical history information, analyzes the medical history text and extracts the disease name, diagnosis date, and treatment information, and stores the extracted key information in the database.

[0040] In the physical examination data collection unit, the elderly are prompted to enter their height and weight, calculate the body mass index of the elderly, and store the calculated BMI value in the database.

[0041] In the scale assessment unit, a scale assessment questionnaire is presented to the elderly, each question in the scale is scored according to their own situation, and the total score is calculated according to the scoring criteria of the scale, and the scale total score is stored in the database.

[0042] In the preliminary risk calculation unit, information of the elderly, lifestyle scores, medical history information, BMI values, and total scale scores are extracted from the database. These data are used as inputs, and a logistic regression model is applied to calculate the risk scores, which are then stored in the database.

[0043] In the risk level classification unit, the risk scores are retrieved from the database. According to the preset risk score thresholds, the elderly are classified into low-risk, medium-risk, or high-risk levels, and the risk level information is updated in the database.

[0044] In the risk report generation unit, personal information, assessment results, risk levels, and intervention suggestions of the elderly are collected. These information are filled into a pre-designed report template to generate a risk report, which is then provided to the elderly.

[0045] In the risk warning and intervention suggestion unit, according to the risk levels of the elderly, the intervention suggestion rule base is queried to extract warning information and intervention suggestions that match the risk levels of the elderly. The warning information and intervention suggestions are displayed to the elderly, and corresponding health actions are recommended.

[0046] More specifically, the total score calculation formula of the Likert scale:

[0047]

[0048] where n is the number of questions in the scale.

[0049] The logistic regression model formula for calculating risk probability:

[0050] logit=(p(y=1))=β 0 +β 1 X 1 +β 2 X 2 +...+β n X n

[0051] where p(y=1) is the probability of the event occurring, X 1 ,X 2 ,...,X n X 1 ,X 2 ,...,X n are independent variables, and β 0 ,β 1 ,...,β n β 0 ,β 1 ,...,β n are model parameters.

[0052] Risk level classification formula:

[0053] When the risk score ≤ threshold 1, the risk level is low risk; when threshold 1 < risk score ≤ threshold 2, the risk level is medium risk; when the risk score > threshold 2, the risk level is high risk.

[0054] The multi-dimensional health assessment module includes a physiological function assessment unit, a psychological state assessment unit, a social participation assessment unit, a nutritional status assessment unit, a chronic disease management assessment unit, a drug use assessment unit, a functional ability assessment unit, a disease risk prediction unit, and a comprehensive assessment report generation unit;

[0055] The physiological function assessment unit is used to assess the cardiopulmonary function, muscle strength, balance ability, and sensory function of the elderly; the psychological state assessment unit is used to assess the cognitive function, emotional state, mental health status of the elderly through psychological scales, as well as whether there are depression and anxiety problems; the social participation assessment unit is used to assess the frequency and degree of the elderly's participation in social activities; the nutritional status assessment unit assesses the nutritional intake and nutritional status of the elderly through dietary surveys, nutritional status questionnaires, or biochemical index tests; the chronic disease management assessment unit assesses whether the elderly have chronic diseases and the management and control of these diseases; the drug use assessment unit assesses the types, doses, frequencies of drugs currently used by the elderly, as well as the compliance of drug treatment and possible adverse reactions; the functional ability assessment unit assesses the self-care ability and independent living ability of the elderly through activities of daily living and instrumental activities of daily living; the disease risk prediction unit uses historical data and prediction models to assess the risk of the elderly suffering from specific diseases in the future; the comprehensive assessment report generation unit integrates the assessment results of the above-mentioned units to generate a comprehensive health assessment report.

[0056] Specifically, in the physiological function assessment unit, the heart rate, blood pressure, and pulmonary function test results of the elderly are detected by physiological parameter detection equipment, the age and gender data are input, and the pre-trained physiological function assessment model is applied to output the physiological function score.

[0057] In the psychological state assessment unit, the psychological state assessment questionnaire is distributed and guided to be completed, and the questionnaire answers are collected. The questionnaire answers are encoded into a numerical format and input into the pre-trained psychological state assessment model to output the psychological state score.

[0058] In the social participation assessment unit, social activity data are collected through a structured questionnaire, and the types and frequencies of activities participated in are recorded.

[0059] In the nutritional status assessment unit, past dietary records are collected, blood and biochemical indexes are collected, the dietary intake and biochemical index data are input into the nutrition assessment system, the actual intake is compared with the recommended intake standard, and the nutritional status score is output.

[0060] In the chronic disease management assessment unit, chronic disease diagnosis and treatment records are collected, disease control indicators are recorded, the disease control effect is evaluated, and the chronic disease management score is output.

[0061] In the drug use assessment unit, drug prescriptions and medication records are obtained, the interactions between drugs are analyzed, the patient's drug compliance is evaluated, and the drug use assessment algorithm is applied to output the drug use score.

[0062] In the functional ability assessment unit, ADLs are evaluated through questionnaires, IADLs are evaluated through questionnaires, the ADLs and IADLs data are encoded into numerical values, input into the functional ability assessment model, and the functional ability score is output.

[0063] In the disease risk prediction unit, historical health data and disease progression records are collected, the disease risk prediction model is used, input into the model for risk prediction, and the future disease risk probability is output.

[0064] In the comprehensive assessment report generation unit, the assessment results of all units are collected, the result data is formatted, the assessment results are filled into the report template, and the comprehensive assessment report is output.

[0065] The dynamic monitoring and feedback adjustment module includes a real-time health data monitoring unit, a data analysis and anomaly detection unit, a risk warning trigger unit, a feedback information generation unit, a health management plan adjustment unit, and a family and medical service coordination unit;

[0066] Specifically, in the real-time health data monitoring unit, the data transmission function of the wearable device is activated, real-time health data is received through a wireless protocol, and the received data is stored in a time series database in timestamp order.

[0067] In the data analysis and anomaly detection unit, health data within a certain time window is extracted from the database, the statistical characteristics of the data points are calculated, the Z-score algorithm is applied to determine whether the data points are abnormal, and the detailed information of the abnormal data points is recorded.

[0068] In the data analysis and anomaly detection unit, health data within a certain time window is extracted from the database, the statistical characteristics of the data points are calculated, the Z-score algorithm is applied to determine whether the data points are abnormal, and the detailed information of the abnormal data points is recorded.

[0069] In the risk warning trigger unit, set the warning threshold for abnormal data, compare the score of the abnormal data point with the warning threshold, and when the abnormal score exceeds the threshold, activate the warning system and send an alarm.

[0070] In the feedback information generation unit, select the corresponding feedback information template according to the abnormal type and severity. Fill in the variables in the template to generate specific feedback information.

[0071] In the health management plan adjustment unit, obtain the current health management plan parameters, and determine the parameters that need to be adjusted according to the real-time data and the feedback from the elderly.

[0072] In the family and medical service coordination unit, construct a communication list including family members and medical service providers, send coordination notifications through the message queue system, so that all recipients can receive the information, and record the communication log.

[0073] More specifically, the comprehensive score calculation:

[0074]

[0075] Among them, w i is the weight of the i th evaluation unit, and S i is the score of the i th evaluation unit.

[0076] The dynamic monitoring and feedback adjustment module includes a data integration unit, a risk assessment and classification unit, a decision rule base management unit, a decision support algorithm unit, a result interpretation and presentation unit, and an intervention plan recommendation unit;

[0077] The data integration unit summarizes and integrates the data from different evaluation modules to create a complete health record; the risk assessment and classification unit uses statistical models and machine learning algorithms to conduct risk assessment on the integrated data and classifies the elderly into different risk levels; the decision rule base management unit is used to maintain and update the rule base containing health management and intervention suggestions; the decision support algorithm unit applies algorithms and combines the decision rule base to provide health management and intervention suggestions for the elderly; the result interpretation and presentation unit converts the output of the decision support algorithm into text descriptions and charts; the intervention plan recommendation unit recommends drug treatment and lifestyle changes based on the risk assessment results and decision rules.

[0078] Specifically, in the data integration unit, define the data source and the target data format, extract the original data from each data source, convert the original data into the target format, and load the converted data into the central data warehouse.

[0079] In the risk assessment and classification unit, risk assessment is carried out through a machine learning model. The model is trained using a training data set, and the performance of the model is evaluated through a cross-validation method, and the evaluation score of the model is recorded.

[0080] In the decision rule base management unit, the decision rule base is created and updated, and new rules and updated rules are added to the rule base.

[0081] In the decision support algorithm unit, the model is trained using feature data and label data, and the trained model is saved.

[0082] In the result interpretation and presentation unit, a visualization chart of the result is generated through data visualization for display.

[0083] In the intervention plan recommendation unit, a preliminary intervention plan is generated based on the risk profile, an optimization algorithm is applied to select the best intervention combination, and the optimized intervention plan is output.

[0084] The comprehensive decision support module includes a risk scoring unit, a clinical decision rule engine, a recommendation generation unit, a decision execution monitoring unit, and a feedback loop unit;

[0085] The risk scoring unit calculates the overall health risk score of the elderly based on the aggregated data for identifying high-risk individuals; the clinical decision rule engine applies clinical guidelines and practices to assist in decision-making through a preset rule set; the recommendation generation unit generates health management recommendations based on the individual's health risk score and clinical rules; the decision execution monitoring unit is used to track the decision execution situation and monitor the implementation effect; the feedback loop unit is used to collect feedback information after the decision is executed.

[0086] Specifically, in the clinical decision rule engine, clinical decision rules are defined, and for each rule, it is checked whether the patient data meets the conditions. When the conditions are met, the corresponding decision actions are executed, and all decision actions that trigger the rules are collected.

[0087] In the recommendation generation unit, the input parameters for recommendation generation are determined based on the risk score and clinical decision results, and the health recommendations are generated and formatted for output.

[0088] Logistic regression formula for classification:

[0089] [P(Y=1)1 / (1+e^(beta_0+beta_1*X_1+…+beta_n*X_n))]

[0090] Where, (P(Y=1)) is the probability that the target variable is 1, (e) is the base of the natural logarithm, (beta_0,beta_1,…,beta_n) are model parameters, and (X_1,X_2,…,X_n) are feature variables.

[0091] Example Two

[0092] Please refer to Figure 5 , a medical health assessment method for the elderly, comprising the following steps:

[0093] Step 1: Collect the age, gender, medical history, family medical history, and lifestyle information of the elderly, measure height, weight, blood pressure, and heart rate, evaluate the eating habits, exercise frequency, smoking and drinking status of the elderly, and conduct a preliminary analysis of the collected data to identify obvious health risks or problems;

[0094] Step 2: Conduct pulmonary function tests, cardiac function tests, and muscle strength tests through medical devices, use the MMSE and depression scales to evaluate cognitive function and emotional state, understand the social activity participation of the elderly to evaluate their social network, evaluate the nutritional status of the elderly through dietary surveys and blood tests, and evaluate the control and management effects of chronic diseases based on past medical history;

[0095] Step 3: Calculate a comprehensive health risk score for the elderly based on the evaluation results, set specific health management goals according to the risk score and evaluation results, formulate intervention measures to implement a health management plan for the health problems found in the evaluation, and regularly monitor the effects and adjust the plan according to the feedback.

[0096] In addition, in each embodiment of the present invention, each functional unit can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0097] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A medical health assessment system for the elderly, characterized by: include: Data collection and preprocessing module, initial risk assessment module, multi-dimensional health assessment module, dynamic monitoring and feedback adjustment module, comprehensive decision support module, health management plan generation module, system verification and iteration module, elderly interface and interaction module, security and privacy protection module.

2. A medical health assessment system for the elderly according to claim 1, characterized in that: The initial risk assessment module includes information collection unit, lifestyle information collection unit, disease history collection unit, physical examination data collection unit, scale assessment unit, preliminary risk calculation unit, risk level classification unit, risk report generation unit, and risk warning and intervention suggestion unit; Information collection unit, used to collect the patient's age, gender, marital status, education level, and occupational background; Lifestyle information collection unit, used to record the patient's eating habits, exercise frequency, smoking and drinking habits, and sleep quality; medical history collection unit, used to collect the patient's past medical history, family medical history, surgical history, and drug allergy history; physical examination data collection unit, used to record the patient's height, weight, blood pressure, heart rate, and blood sugar level; scale assessment unit, used to assess the patient's cognitive function, daily living ability, and depressive symptoms through the Mini-Mental State Examination (MMSE) and the Daily Living Ability Scale (ADL); preliminary risk calculation unit, based on the collected data, uses a preset risk assessment algorithm to calculate the patient's specific health risk score; The risk level classification unit classifies patients into different risk levels based on the risk calculation results; A risk report generation unit, used to generate assessment results; The risk warning and intervention recommendation unit provides warning information for patients assessed as high-risk and gives preliminary intervention recommendations based on the risk type.

3. The medical health assessment system for the elderly according to claim 1, characterized in that: The multi-dimensional health assessment module includes physiological function assessment unit, psychological state assessment unit, social participation assessment unit, nutritional status assessment unit, chronic disease management assessment unit, drug use assessment unit, functional ability assessment unit, disease risk prediction unit, and comprehensive assessment report generation unit; The physiological function assessment unit is used to assess the cardiopulmonary function, muscle strength, balance ability, and sensory function of the elderly; the psychological state assessment unit is used to assess the cognitive function, emotional state, mental health status, and whether there are depression and anxiety problems of the elderly through psychological scales; the social participation assessment unit is used to assess the frequency and degree of participation of the elderly in social activities; the nutritional status assessment unit assesses the nutritional intake and nutritional status of the elderly through dietary surveys, nutritional status questionnaires or biochemical indicator tests; the chronic disease management assessment unit assesses whether the elderly suffer from chronic diseases and the management and control of these diseases; the drug use assessment unit assesses the types, dosages, and frequencies of drugs currently used by the elderly, as well as compliance with drug treatment and possible adverse reactions; the functional ability assessment unit assesses the self-care ability and independent living ability of the elderly through daily living activities and instrumental daily living activities; the disease risk prediction unit uses historical data and prediction models to assess the risk of the elderly suffering from specific diseases in the future; the comprehensive assessment report generation unit integrates the assessment results of the above units to generate a comprehensive health assessment report.

4. The medical health assessment system for the elderly according to claim 1, characterized in that: The dynamic monitoring and feedback adjustment module includes a real-time health data monitoring unit, a data analysis and anomaly detection unit, a risk warning trigger unit, a feedback information generation unit, a health management plan adjustment unit, and a family and medical service coordination unit; The real-time health data monitoring unit collects the elderly’s vital signs and environmental data in real time through wearable devices; The data analysis and anomaly detection unit analyzes the data collected in real time, identifies normal fluctuation patterns and potential abnormal patterns, and detects possible health problems; The risk warning trigger unit triggers the warning mechanism when abnormal data is detected or the health risk exceeds the preset threshold; The feedback information generation unit generates health advice, guidance on adjusting lifestyle and reminders on drug use based on monitoring results and early warning information; The health management plan adjustment unit adjusts the health management plan for the elderly based on real-time monitoring data and feedback information; The Family and Health Care Coordination Unit coordinates communication between family members and health care providers to ensure that the health management plan for the elderly is effectively implemented and supported.

5. The medical health assessment system for the elderly according to claim 1, characterized in that: The dynamic monitoring and feedback adjustment module includes a data integration unit, a risk assessment and classification unit, a decision rule base management unit, a decision support algorithm unit, a result interpretation and presentation unit, and an intervention plan recommendation unit; The data integration unit aggregates and integrates data from different assessment modules to create a complete health record; the risk assessment and classification unit uses statistical models and machine learning algorithms to conduct risk assessment on the integrated data and classify the elderly into different risk levels; the decision rule base management unit is used to maintain and update the rule base containing health management and intervention suggestions; the decision support algorithm unit applies algorithms and combines the decision rule base to provide health management and intervention suggestions for the elderly; The result interpretation and presentation unit converts the output of the decision support algorithm into text descriptions and charts; the intervention plan recommendation unit recommends drug treatment and lifestyle changes based on risk assessment results and decision rules.

6. The medical health assessment system for the elderly according to claim 1, characterized in that: The integrated decision support module includes a risk scoring unit, a clinical decision rule engine, a recommendation generation unit, a decision execution monitoring unit, and a feedback loop unit; The risk scoring unit calculates the overall health risk score of the elderly based on the aggregated data to identify high-risk individuals; Clinical decision rules engine, which applies clinical guidelines and practices to assist decision making through preset rule sets; The suggestion generation unit generates health management suggestions based on the individual's health risk score and clinical rules; Decision execution monitoring unit, used to track the execution of decisions and monitor the implementation effect; Feedback loop unit, used to collect feedback information after decision execution.

7. A method for medical health assessment of the elderly, applied to the medical health assessment system for the elderly as claimed in any one of claims 1 to 6, characterized in that: The following steps are involved: Step 1: Collect information on the elderly’s age, gender, medical history, family medical history, and lifestyle; measure height, weight, blood pressure, and heart rate; assess the elderly’s eating habits, exercise frequency, and smoking and drinking habits; conduct a preliminary analysis of the collected data to identify obvious health risks or problems; Step 2: Perform lung function tests, heart function tests, and muscle strength tests through medical equipment, use MMSE and depression scales to assess cognitive function and emotional state, understand the elderly's participation in social activities to assess their social network, assess the nutritional status of the elderly through dietary surveys and blood tests, and assess the control and management of chronic diseases based on past medical history; Step 3: Based on the assessment results, calculate a comprehensive health risk score for the elderly. Set specific health management goals based on the risk score and assessment results. Develop intervention measures to implement a health management plan for health problems found in the assessment. Regularly monitor the results and adjust the plan based on feedback.

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