Multi-modal AI model for physical examination of old people

Abstract

The invention relates to the field of multi-modal models, and discloses a multi-modal AI model for health examination of old people, which is used for providing a comprehensive solution for health management of the old people. Comprising the following steps: inputting electrocardiogram signals and other data into a model constructed based on a continuous medium mechanics principle, generating physiological state tensors, inputting the data into a semantic association engine, generating an association feature matrix through small sample processing and the like, simulating multi-factor interaction, and generating a risk index and an early warning signal; inputting an intervention strategy engine, and generating a health management scheme in combination with the individual features; and the data and real-time physical examination data are jointly input into a state monitoring system, so that self-adaptive optimization of a health management strategy is realized. According to the health management scheme, regional medical resource data and the like, adaptability evaluation, path planning, risk prevention and control and effect tracking are carried out, an evaluation report, a planning graph, a prevention and control scheme set, a collaborative execution network graph and the like are generated, and a comprehensive, accurate and continuously optimized solution is provided for health management of old people.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of multi-modal models, and particularly relates to a multi-modal AI model for health examination of the elderly. BACKGROUND

[0002] With the intensification of global population aging, the number of elderly population is growing rapidly, and the health problems of the elderly are increasingly becoming the focus of social attention. The elderly population often faces multiple chronic diseases due to the decline of physical function, such as cardiovascular disease, diabetes, respiratory disease, etc. These diseases not only seriously affect the quality of life of the elderly, but also bring heavy medical burden to families and society. Therefore, how to effectively manage the health of the elderly and achieve early prevention, early diagnosis and early intervention of diseases has become an important problem to be solved in the current medical and health field.

[0003] In the information age, information technology is increasingly widely used in the field of medical and health care, providing new ideas and methods for health management of the elderly. Information technology consulting services such as information technology management consulting and information planning are constantly developing in the medical industry, aiming to integrate various medical resources, optimize medical processes, and improve medical service efficiency and quality. At the same time, the development of customer interaction services enables medical institutions to better communicate with the elderly and their families, understand their health needs and feedback, and provide strong support for the development of personalized health management programs.

[0004] The existing health management technology for the elderly still has many shortcomings: Traditional health examination methods are often limited to single-modal medical data collection, relying only on electrocardiogram, B-ultrasound or laboratory test results, etc. for a single examination, which is difficult to comprehensively and accurately reflect the physiological state and health risks of the elderly. There is a lack of effective correlation and integration between different modalities of medical data, resulting in insufficient information utilization and inability to provide comprehensive and accurate diagnostic basis for doctors; Existing health risk assessment methods are mostly based on simple statistical models or experience-based judgments, which are difficult to accurately simulate the complex dynamic changes of the human physiological system, as well as the interaction and long-term cumulative effects of multiple factors. This makes the health risk assessment results often not accurate enough, and unable to timely detect early signs of chronic diseases, thus delaying the best treatment opportunity; In terms of the development of health management programs, existing technologies often lack individualization and do not fully consider individual characteristics, living habits and realistic constraints of the elderly, resulting in difficulty in effectively implementing the developed programs in real life and failing to achieve the expected health management effect. At the same time, there is a lack of effective monitoring and evaluation mechanism for the implementation effect of health management programs, making it difficult to adjust and optimize the programs in a timely manner to adapt to the changes in the health status of the elderly.

[0005] Therefore, we propose a multimodal AI model for health checkups of the elderly to address the above problems. Summary of the Invention

[0006] This invention provides a multimodal AI model for health checkups of the elderly, offering a comprehensive solution for health management of the elderly.

[0007] The first aspect of this invention provides a multimodal AI model for health checkups of the elderly. The multimodal AI model for health checkups of the elderly includes: a physiological module, used to input collected electrocardiogram signals, ultrasound images, and laboratory test data into a physiological system dynamics model, generating a physiological state tensor by simulating the stress-strain relationship and energy transfer process of the human physiological system; a mapping module, used to establish mapping relationships between different modalities of medical data based on the physiological state tensor, generating an association feature matrix through small sample trend extraction and uncertainty processing; an allocation module, used to input the association feature matrix into a health risk assessment model, generating a health risk index and early warning signals for chronic diseases by simulating the dynamic interaction and long-term cumulative effects of multiple factors; an intervention module, used to input the health risk index and early warning signals for chronic diseases into an intervention strategy engine, combining individual characteristic parameters of the elderly, generating health management plans and lifestyle guidance suggestions by simulating the behavioral impact paths of different intervention measures; and an early warning module, used to generate dynamically updated early warning information and adjusted health management plans based on the health management plan and real-time collected health checkup data, by detecting health status transition patterns and intervention effect feedback.

[0008] Optionally, in the first implementation of the first aspect of the present invention, the method includes: constructing a physiological parameter association network reflecting the interaction between various physiological parameters based on the constitutive relation theory in continuum mechanics, according to the characteristic parameters of electrocardiogram signals, structural parameters of B-ultrasound images, and biochemical index parameters of laboratory reports; generating a physiological system network model; analyzing the stress transmission path and strain response characteristics between various physiological subsystems based on the node connection relationships in the physiological system network model; generating a stress distribution matrix; simulating the energy flow and transformation process of the cardiovascular, metabolic, and immune systems based on the thermodynamic energy conservation principle using the stress distribution matrix; generating an energy state vector characterizing the energy state of the system; establishing an evolution equation of the physiological state based on the energy state vector and real-time acquired physiological time-series data; generating a physiological state tensor by solving the equilibrium state and dynamic response of the system; and verifying the physiological state tensor by comparing it with the normal physiological range parameters in the medical knowledge base, performing quality control on the rationality and consistency of the tensor data, and generating a quality-controlled and verified physiological state tensor.

[0009] Optionally, in the second implementation of the first aspect of the present invention, the method includes: constructing a semantic membership function based on fuzzy set theory using a physiological state tensor; establishing a fuzzy mapping relationship between electrocardiogram features, ultrasound image features, laboratory indicators, and clinical semantic concepts; generating a multimodal semantic mapping rule set; calculating the grey relational degree between each modality data sequence based on the multimodal semantic mapping rule set and real-time acquired medical data; generating a relational degree tensor; performing trend mining and feature enhancement on finite sample data based on the relational degree tensor; generating a trend feature vector; processing uncertainties and contradictory information in the data based on the trend feature vector and the semantic mapping rule set; generating a fusion feature set; constructing a multimodal feature association structure based on the fusion feature set; verifying semantic rationality through a medical knowledge base; and generating an association feature matrix.

[0010] Optionally, in a third implementation of the first aspect of the present invention, the method includes: constructing a multi-factor interaction network based on the correlation feature matrix and system dynamics theory; simulating the dynamic interaction between factors by establishing positive and negative feedback loops to generate a system state evolution vector; analyzing the long-term cumulative effect of chronic disease risk factors based on the system state evolution vector; calculating the cumulative exposure of each risk factor to generate a risk cumulative effect field; and identifying the transmission path and key nodes of risk in the physiological system based on the risk cumulative effect field, determining the main risk transmission channels, and generating a risk transmission path map. By using risk transmission pathway maps and real-time monitored physiological parameters as inputs, the health risk levels at the organ, system, and overall levels are assessed to generate a hierarchical set of risk quantification indicators. Based on the hierarchical risk quantification indicator set, abnormal patterns exceeding the threshold are identified, and clinical rationality is verified by combining the medical knowledge base to generate a health risk index and early warning signals for chronic diseases.

[0011] Optionally, in the fourth implementation of the first aspect of the present invention, the method includes: constructing a virtual agent model reflecting individual physiological characteristics and behavioral patterns based on health risk index, early warning signals, and basic information and lifestyle data of the elderly as input, and generating an individual agent model; simulating the behavioral response paths of different intervention measures on the individual agent model based on the individual agent model and various health intervention programs in the intervention measure library, and generating an intervention measure behavior influence network diagram; evaluating the comprehensive effect of each intervention program from three dimensions—compliance, effectiveness, and safety—based on the intervention measure behavior influence network diagram, and generating an optimized and ranked sequence of intervention programs; taking the optimized and ranked sequence of intervention programs and the characteristic parameters of the individual agent model as input, selecting the intervention combination most suitable for the current individual characteristics of the elderly, and generating a preliminary personalized health management plan; and making realistic adaptive adjustments to the plan based on the preliminary personalized health management plan and the actual constraints of the elderly, and generating a health management plan and lifestyle guidance suggestions.

[0012] Optionally, in the fifth implementation of the first aspect of the present invention, the comprehensive effect of each intervention scheme is evaluated from three dimensions: compliance, effectiveness, and safety. : .

[0013] Optionally, in the sixth implementation of the first aspect of the present invention, the method includes: constructing a health state transition probability model based on the health management plan and real-time collected physical examination data; identifying the transition patterns of health states through observation sequence analysis; generating a health state transition probability matrix; constructing a three-level monitoring network including a physiological indicator layer, a functional state layer, and an overall health layer based on the system monitoring theory, and generating a health state monitoring network based on the health state transition probability matrix; analyzing the actual effect of intervention measures by combining the health state monitoring network with the effect data after implementing the health management plan, and generating an intervention effect feedback vector; recalibrating the original early warning signal based on the intervention effect feedback vector, and generating an updated dynamic early warning information set through a threshold adaptive adjustment mechanism; and analyzing the impact of different adjustment schemes on health state transition based on the dynamic early warning information set and the health state transition probability matrix, and generating an optimized and adjusted health management plan.

[0014] Optionally, in the seventh implementation of the first aspect of the present invention, an evaluation module is further included: based on the health management plan and data on equipment configuration, personnel qualifications, and service capabilities in the regional medical resource database, the resource conditions required for the implementation of the plan are analyzed, and a medical resource suitability assessment report is generated; based on the medical resource suitability assessment report, a trade-off optimization is performed among the three objectives of optimal effect, lowest cost, and highest feasibility, and a personalized health management implementation path planning diagram is generated; based on the implementation path planning diagram, obstacles and risk points that may be encountered during the implementation of the plan are identified, and a risk prevention and control plan set including contingency plans is generated; based on the risk prevention and control plan set and organizational structure data of primary medical institutions, a cross-departmental and cross-level plan execution collaboration network is constructed, and a collaborative execution network diagram is generated; the collaborative execution network diagram and execution log data during the implementation of the plan are used to construct an effect tracking system, and a health management effect evaluation report is generated.

[0015] Optionally, in the eighth implementation of the first aspect of the present invention, a trade-off optimization is performed among the three objectives of optimal effect, lowest cost, and highest feasibility. for : Where E represents the expected result and C represents the estimated cost. F represents the maximum acceptable cost and the feasibility factor.

[0016] The mechanism of this invention is as follows: applying the stress-strain relationship in continuum mechanics to physiological system modeling to establish a mechanical correlation network between physiological parameters; using fuzzy logic and grey system theory to handle uncertainties and small sample problems in medical data to achieve cross-modal semantic mapping; simulating multi-factor dynamic interactions based on system feedback theory to identify risk transmission paths; and using surrogate simulation technology to construct a virtual individual model to predict the behavioral impact of different intervention measures. While ensuring the model's lightweight nature, it achieves intelligent management of the entire process from data collection to intervention optimization, providing an interpretable and implementable proactive health management solution for primary healthcare. Beneficial effects: Multimodal data fusion can capture information that cannot be reflected by a single data modality, reduce misdiagnosis and missed diagnosis caused by data bias, improve the accuracy of diagnosis of health problems in the elderly, and help to detect potential health risks at an early stage; In the semantic association engine, based on fuzzy logic and grey system theory, by constructing semantic membership functions, calculating grey relational degree, and performing grey prediction, it is possible to perform trend mining and feature enhancement on limited small sample data. It has important application value when the sample size of health data collection for the elderly may be limited, and can make fuller use of existing data to mine valuable information. Based on the health management plan and regional medical resource database, the resource conditions required for the implementation of the plan are analyzed, and a medical resource suitability assessment report is generated. This helps to rationally allocate medical resources, avoid resource waste or shortage, improve resource utilization efficiency, and rationally allocate information technology resources in information planning to ensure the efficient operation of the system. Based on multi-objective decision-making theory, a personalized health management implementation path planning map is generated by balancing and optimizing the three objectives of optimal effect, lowest cost, and highest feasibility. Then, based on the implementation path planning map and risk matrix theory, potential obstacles and risks encountered during the implementation process are identified, generating a risk prevention and control plan set including contingency plans. A cross-departmental and cross-level collaborative network for plan execution and an effect tracking system are constructed to ensure the smooth implementation of the health management plan and the achievement of its expected results. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of an embodiment of a multimodal AI model for health checkups of the elderly in this invention. Figure 2 This is a schematic diagram of another embodiment of the multimodal AI model for health checkups of the elderly in this invention; Figure 3 This is a schematic diagram of one embodiment of a multimodal AI model device for health checkups of the elderly, as described in this invention. Detailed Implementation

[0018] This invention provides a multimodal AI model for health checkups of the elderly, offering a comprehensive solution for elderly health management. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0019] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the multimodal AI model for health checkups of the elderly in this invention includes: 101. Based on dynamic system theory, a unified model of physiological state is constructed by inputting the collected electrocardiogram signals, B-ultrasound images, and laboratory test data into a physiological system dynamic model based on the principle of continuous medium mechanics. By simulating the stress-strain relationship and energy transfer process of the human physiological system, a physiological state tensor that uniformly represents the comprehensive physiological state of the elderly is generated. It is understood that the executing entity of this invention can be a multimodal AI model device for health checkups of the elderly, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.

[0020] It should be noted that, taking a 70-year-old male as an example, he has a history of mild hypertension, and his physical examination data is as follows.

[0021] Electrocardiogram (ECG) signal: A 10-second lead II signal was acquired, showing an average heart rate of 72 bpm, a PR interval of 0.16 seconds, a QTc corrected interval of 0.42 seconds, and occasional premature atrial contractions (5 times per hour). Ultrasound imaging: Echocardiography showed a left ventricular end-diastolic diameter of 50 mm, an ejection fraction of 60%, normal ventricular wall thickness (interventricular septum thickness 10 mm), and no valvular regurgitation or stenosis. Laboratory test results: Blood test results included total cholesterol 190 mg / dL, LDL cholesterol 110 mg / dL, fasting blood glucose 95 mg / dL, and serum creatinine 1.0 mg / dL.

[0022] The aforementioned multimodal data were preprocessed and normalized. Electrocardiogram (ECG) signals were converted into time-series features of cardiac electrical activity, heart rate variability (RMSSD of 30 ms), and electrical impulse conduction patterns. Ultrasound images were segmented to extract cardiac structure and motion parameters, including ventricular wall strain rate (15% peak systolic strain). Laboratory data were quantified into biochemical index vectors reflecting metabolic and circulatory status. These data were then input into a physiological system dynamics model constructed based on the principles of continuum mechanics. This model treats the human body as a continuum, simulating the mechanical behavior at the organ and tissue levels. Stress-strain simulation: Using ultrasound data of the heart's structure, the mechanical stress of the left ventricular wall during systole is calculated (based on wall stress distribution simulated by blood pressure, with a resting blood pressure of 130 / 80 mmHg and a peak stress of approximately 120 kPa). Combined with the electrical activity sequence from an electrocardiogram, the strain response of myocardial fibers (diastolic strain energy density) is simulated. Simultaneously, cholesterol levels from laboratory tests affect vascular stiffness parameters, and the model updates the strain characteristics of the vascular wall through constitutive relations. Energy transfer process simulation: Based on blood glucose and creatinine data from laboratory tests, metabolic energy conversion efficiency (oxygen consumption rate) is inferred and combined with cardiac output data from ultrasound to simulate the energy transfer pathway of the cardiovascular system (kinetic and potential energy conversion of blood flow). The model dynamically integrates these processes, considering age-related tissue degeneration (decreased arterial compliance).

[0023] Generate a physiological state tensor: Output a three-dimensional physiological state tensor as a unified representation. The tensor dimensions include time points (intervals of heartbeat cycles), anatomical regions (heart, blood vessels, metabolic system), and physiological indicators (stress value, strain energy, metabolic level). Specifically, for the elderly patient in this case, the tensor might include: at time t=0 seconds, the stress value of the heart region is 0.85 (normalized value), and the strain energy is 0.72; the elastic parameter of the blood vessel region is 0.63; and the energy efficiency of the metabolic system is 0.91. The tensor elements are dynamically updated through continuous simulation, thereby capturing the integrity and interactivity of the comprehensive physiological state.

[0024] 102. Multimodal data semantic association and feature fusion: Based on the physiological state tensor input, a semantic association engine constructed based on fuzzy logic and grey system theory is established to establish the mapping relationship between different modal medical data. Through small sample trend extraction and uncertainty processing, an association feature matrix that integrates multi-source information is generated. It should be noted that the input for this step is the "physiological state tensor" generated in step 101. This tensor has transformed the electrocardiogram, ultrasound images, and laboratory test data of the 70-year-old male into a dynamic representation based on continuous medium mechanics. The tensor includes spatiotemporal dynamic indicators such as normalized peak myocardial stress (0.85), ventricular wall strain energy (0.72), vascular elasticity parameters (0.63), and metabolic energy efficiency (0.91).

[0025] This physiological state tensor is input into a semantic association engine built on fuzzy logic and grey system theory for processing. The engine first addresses the uncertainty and fuzziness in the data. For example, the vascular elasticity parameter in the tensor is 0.63, a precise value, but its clinical significance is "mildly reduced elasticity." The fuzzy logic module assigns a semantic membership degree to this value: 0.3 for the "normal" set and 0.7 for the "mildly reduced" set. Similarly, myocardial stress of 0.85 might be mapped to the semantic label "high load." In this way, tensor indicators with different physical meanings (mechanical, metabolic) are uniformly transformed into comparable fuzzy semantic sets such as "normal / low / high," initially establishing a comparable mapping relationship between different modalities of data.

[0026] Small-sample trend extraction based on grey system theory: Addressing the relatively sparse (small sample) nature of individual elderly patient data in time series, this engine utilizes grey system theory. It generates sequences from limited current tensor data, predicts their short-term evolution trends, and, combined with the patient's history of hypertension, analyzes the correlation between the current myocardial stress and vascular elasticity grey sequences. It finds that "weakened vascular elasticity" is a strongly correlated factor leading to "increased myocardial stress" (correlation degree approximately 0.75). Although only a single physical examination data point is available, it can be inferred that without intervention, this vicious cycle tends to intensify.

[0027] Integrated RAG Medical Knowledge Retrieval and Verification: To ensure the medical accuracy of the above associations, the integrated RAG technology searches the medical knowledge base in real time. When the engine identifies the association between "increased myocardial stress" and "decreased vascular elasticity parameters," the RAG module retrieves authoritative literature describing the pathophysiological mechanisms of "arteriosclerosis and cardiac afterload," providing theoretical support for this association and using the concept of "early changes in hypertensive heart disease" as a potential background association.

[0028] Uncertainty handling and feature fusion: This process integrates all information after fuzzy semantic mapping, grey trend analysis, and knowledge verification. It evaluates the confidence level of different associations (the confidence level of associations based on mechanical data is 85%, while the confidence level of associations based solely on metabolic indicators from laboratory reports may be 70%), and then performs weighted synthesis.

[0029] The final result is a "correlation feature matrix". The rows of this matrix represent different physiological subsystems (cardiovascular biomechanics system, metabolic system), and the columns represent the fused feature dimensions (load level, functional compensation degree, trend risk coefficient, etc.). Each element in the matrix is ​​no longer an isolated measurement value, but a quantitative feature that integrates multi-source information and the strength of their correlations.

[0030] In this example, the matrix contains a feature value of 0.78 (range 0-1, the higher the value, the greater the risk). This index is calculated by combining myocardial stress, vascular elasticity, blood pressure history, and retrieved medical knowledge. It quantitatively reflects the deep connection between different modal data.

[0031] 103. System dynamics health risk prediction: Input the correlation feature matrix into a health risk assessment model based on system feedback theory, and generate a quantitative health risk index and early warning signal for chronic diseases by simulating the dynamic interaction and long-term cumulative effect of multiple factors. It should be noted that the input is the "association feature matrix" generated in step 102. This matrix has fused and quantified the physiological state of the 70-year-old man from multiple dimensions. The matrix includes feature values ​​such as "cardiovascular coupling load index: 0.78", "metabolic-circulatory function compensation degree: 0.65", and "annualized trend coefficient of ventricular wall thickening based on gray trend prediction: 0.05".

[0032] This correlation feature matrix is ​​input into a health risk assessment model based on "system feedback theory." The core of this model is to view the human body as a dynamic system in which multiple subsystems (cardiovascular, metabolic systems) interact through positive and negative feedback loops, and to integrate the deep understanding capabilities of PALM technology to simulate the long-term effects of these interactions. Key processing points are as follows: By identifying key feedback loops, it pinpointed a critical "positive feedback" vicious cycle: "decreased vascular elasticity (reflected by characteristic indices) → increased cardiac pumping resistance (reflected by coupling load indices) → long-term compensatory hypertrophy of the myocardium (reflected by trend coefficients) → increased myocardial oxygen consumption, potentially exacerbating vascular endothelial damage → further reducing vascular elasticity." Simultaneously, the model also assesses the "negative feedback" regulatory mechanism, examining the extent to which the current "metabolic-circulatory function compensation (0.65)" buffers this vicious cycle. PALM technology, through understanding a vast amount of medical literature, provides deep prior knowledge into the strength and clinical significance of these interactions, confirming a strong correlation between "cardiovascular coupling load" and the risk of "heart failure."

[0033] Long-term cumulative effect prediction: The model does not perform static evaluation, but rather dynamic extrapolation. Based on the current correlation characteristics, it simulates the operational consequences of the above feedback loop over the next 1, 3, and 5 years. The model will calculate that, under the current trend, without intervention in lifestyle, the current coupling load index of 0.78 may cumulatively rise to 0.88 in 3 years, while the compensation degree may decrease from 0.65 to 0.55, meaning that the compensation potential is about to be exhausted.

[0034] Risk quantification and early warning generation: The model performs final risk quantification by simulating dynamic interactions and cumulative effects.

[0035] Output: Quantitative health risk index and early warning signals for chronic diseases. Health Risk Index: The model generates a comprehensive health risk index, stating that the risk of major adverse cardiovascular events (MACE) in the next 5 years is 15%. This index simulates the comprehensive result of the long-term interaction of multiple factors, including the cardiovascular and metabolic systems, rather than simply the sum of risks from a single indicator. Early Warning Signals for Chronic Diseases: Simultaneously, the model generates more specific early warning signals, pointing to a clear direction of pathological development. Warning signal 1: High risk of early progression of hypertensive heart disease. Basis: Cardiovascular coupling load is already at a high level (0.78), and there is a clear trend of ventricular wall thickening, indicating that the positive feedback loop has been activated.

[0036] Warning Signal Two: A potential pathway to heart failure has emerged (medium risk). Basis: Compensatory functions are being slowly eroded; if the workload increases further, the risk of decompensation rises significantly.

[0037] 104. Personalized health intervention program generation: Based on the health risk index and early warning signals, the intervention strategy engine based on agent simulation, combined with the individual characteristic parameters of the elderly, generates targeted health management programs and personalized lifestyle guidance suggestions by simulating the behavioral impact paths of different intervention measures. It should be noted that the upstream output includes the health risk index (MACE risk index of 15% for the next 5 years) and early warning signals of chronic diseases obtained from step 103 (high risk of early evolution of hypertensive heart disease, medium risk of potential pathway to heart failure). Individual characteristics of the elderly patient: In this case, the patient is a 70-year-old male with a history of mild hypertension, regular daily routine but a preference for strong flavors, low physical activity, good medication adherence but concerns about side effects.

[0038] This information is fed into an intervention strategy engine based on agent simulation. At its core, the engine creates a virtual "digital agent" for the elderly person, possessing their own physiological state (defined by the model results from previous steps) and behavioral characteristics. The engine evaluates and selects the optimal strategy by simulating how different interventions affect the health evolution path of this "digital agent."

[0039] Target decomposition and strategy library matching: The upstream early warning signals are transformed into specific, actionable targets. For "high risk of early progression of hypertensive heart disease," the core objective is "reducing the cardiovascular coupling load." Subsequently, the engine matches various interventions from the strategy library that may achieve this target: medication adjustment (Option A: consider adding a specific drug to the existing antihypertensive medication), dietary intervention (Option B: strictly control salt intake, reducing daily sodium intake to below 2000 mg), and exercise intervention (Option C: introduce 150 minutes of moderate-intensity aerobic exercise per week). Agent simulation and impact path extrapolation: The engine allows a "digital agent" to try these options and simulates their short-term and long-term effects.

[0040] Simulated Option A (Pharmacology Adjustment): The engine predicts that this option can directly reduce vascular resistance, potentially lowering the "cardiovascular coupling load index" from 0.78 to 0.72 within the next 3 months. However, it also simulates patient concerns, assessing that long-term medication adherence may decrease by 5% as a result.

[0041] Simulated Plan B (Strict Salt Control): Based on the patient's "preference for strong flavors," the simulation engine assesses the difficulty of implementation (initial compliance is estimated at 60%). If adherence is maintained, the simulation shows that after 6 months, the load index can be slowly reduced to 0.75 by decreasing blood volume and improving vascular endothelial function, with no negative effects.

[0042] Simulated Plan C (Exercise Intervention): Considering the patient's current situation of "low exercise volume", the simulation starts with low intensity (brisk walking) and gradually increases. The projection path shows that after 3 months of persistence, metabolic efficiency can be improved, and after 6 months, vascular elasticity can be slightly improved, reducing the load index to 0.76 and improving "metabolic-circulatory function compensation".

[0043] Solution Optimization and Integration: The engine doesn't select a single solution but simulates a combination strategy. It found that for this patient, the most feasible and effective path was to prioritize the B+C combination (diet + exercise), as this involves relatively gentle changes to the patient's life and has a synergistic effect. Simultaneously, a monitoring target was set: if the burden index does not drop below 0.76 at the 6-month follow-up examination, then solution A would be initiated. This "step-by-step" strategy respects the patient's current wishes while setting clear escalation conditions.

[0044] Output: Targeted health management plans and personalized lifestyle guidance. The final plan is highly specific and personalized. Core of the health management plan: Initial phase (next 6 months): Primarily non-pharmacological intervention. Core goal: Reduce cardiovascular coupling load by more than 3%. Upgrade conditions: If the load index does not reach the target after 6 months, discuss with a doctor to initiate a medication adjustment plan.

[0045] Personalized Lifestyle Guidance: Diet: Set a daily sodium intake target of 2000 mg. Specific suggestions: Halve the amount of salt used in cooking, avoid pickled vegetables and cured meats, and check the sodium content when buying packaged foods. Exercise: Start with "5 times a week, 20 minutes of brisk walking each time," and transition to "5 times a week, 30 minutes of brisk walking combined with jogging" after two months. Clearly state that this is moderate intensity, and the heart rate should be maintained at around 100 beats per minute during exercise.

[0046] Monitoring and Motivation: It is recommended that families monitor their blood pressure (twice a day, morning and evening) and use the app to record their diet and exercise. The engine specifically points out that adhering to this plan is expected to result in improved energy levels after 3 months, serving as a positive motivation.

[0047] 105. Dynamic monitoring and program optimization: Based on the health management program and real-time collected physical examination data, the system inputs them into a state monitoring system based on a hidden Markov model. By detecting health status transition patterns and intervention effect feedback, the system generates dynamically updated early warning information and optimized health management programs, thereby achieving continuous adaptive optimization of health management strategies.

[0048] Among them, the semantic association engine integrates a medical knowledge retrieval module built with RAG technology to ensure the accuracy of semantic association; the health risk assessment model integrates the multimodal understanding capabilities of PALM technology to improve the accuracy of risk assessment; and the state monitoring system adopts a semi-supervised learning mechanism to achieve continuous iterative optimization of the model using limited labeled data. It should be noted that the input for this step includes two items: Initial health management plan: a personalized plan generated from step 104, the core of which is: to prioritize reducing the cardiovascular coupling load by controlling salt intake (daily sodium <2000 mg) and regular exercise (150 minutes of moderate-intensity aerobic exercise per week) for the next 6 months, and to set a follow-up examination after 6 months. If the load index does not drop below 0.76, drug intervention will be considered.

[0049] Real-time collected physical examination data: Three months after the program was implemented, the patient returned to the community health center for some examinations. The data included: the average home blood pressure dropped to 125 / 78 mmHg, and the patient lost 2 kg. However, a simplified ultrasound examination showed no significant change in ventricular wall thickness. At the same time, the patient reported through the APP that exercise adherence reached 80%, but dietary salt control adherence was only 50%.

[0050] The aforementioned new and old data are input into a state monitoring system based on a Hidden Markov Model. This system defines the health status of older adults as a series of potential, inter-transferable states ("compensatory stabilization period," "burden increase period," "intervention response period," etc.), and infers the most likely state and the probability of future transitions based on observed data. Health status transition pattern detection: The system analyzes new data from the past 3 months. Observed signals: Lower blood pressure, weight loss, and high exercise adherence are positive signals; no improvement in ventricular wall thickness and low adherence to salt restriction are negative / neutral signals.

[0051] State inference: The system judges that the patient has a high probability (65%) of transitioning from the "burden increase period" three months ago to the sub-state of "initial effectiveness of exercise, insufficient nutritional intervention" in the "intervention response period". This judgment is based on the fact that the improvement in blood pressure and weight is mainly attributed to the metabolic benefits of exercise, while insufficient salt intake control explains why the cardiac structural load (ventricular wall thickness) has not been reduced synchronously.

[0052] Intervention effect feedback and prediction: Based on the current inferred state, the system predicts the outcome of continuing the original plan. It predicts that due to the poor effect of dietary intervention, the goal of reducing the cardiovascular coupling load index to below 0.76 by 6 months through exercise alone is at high risk (60% probability) of not being achieved, thus triggering a drug upgrade.

[0053] Solution Optimization and Adjustment (Semi-Supervised Learning Mechanism): To avoid unnecessary drug intervention, the system initiates an optimization process. It first utilizes a large amount of similar elderly health trajectory data without clearly labeled outcomes (semi-supervised learning) to identify patterns in successful cases. It finds that for patients with poor dietary adherence but good exercise adherence, adjusting dietary intervention from "general salt restriction" to "specific food substitution strategies" typically improves effectiveness. Simultaneously, the system assigns a temporary, unverified label to this data point: "Dietary guidance needs strengthening." This label will be confirmed or corrected after obtaining final results from more similar cases, thus enabling continuous model iteration.

[0054] Dynamically updated early warning information: Warning content: "Insufficient nutritional intervention may affect the overall effectiveness of initial non-pharmacological interventions, and there is a risk of prematurely initiating pharmacological interventions." Warning basis: Based on inferences from the current state transition and predictions of future pathways.

[0055] Optimized and adjusted health management plan: Core adjustment: The exercise plan is retained, but the specificity of dietary intervention is emphasized.

[0056] New specific guidance: Strategy adjustment: The recommendation has been changed from "suggesting salt control" to "completely replacing regular soy sauce with low-sodium soy sauce three times a week, and adding a serving of unsalted cold vegetables to your dinner to enhance satiety." This specific behavioral instruction is easier to implement and monitor. Monitoring adjustment: Patients are advised to use the app twice a week to take photos of their lunch and dinner for simple dietary records, with the system providing lightweight feedback.

[0057] Goal fine-tuning: Set an intermediate goal within 3 months: increase diet adherence from 50% to 70%.

[0058] In this embodiment of the invention, a dynamic model of the physiological system is constructed based on dynamic system theory and the principles of continuous medium mechanics to simulate the complex mechanical behavior of the human physiological system. Different types of data are uniformly transformed into physiological state tensors, achieving a unified representation of physiological states and improving the systematicness and scientific rigor of the assessment. Fuzzy logic is used to handle data uncertainty and fuzziness, transforming indicators with different physical meanings into comparable fuzzy semantic sets. Based on grey system theory, small-sample trend extraction is performed to predict short-term evolution trends from limited data, analyze the correlation between different indicators, and mine deeper data information, providing a more reliable basis for health risk assessment. RAG is integrated. The technology enables real-time retrieval of medical knowledge bases, providing theoretical support for semantic association, ensuring the accuracy of associative medicine, and enhancing the interpretability and credibility of the model. A health risk assessment model is constructed based on system feedback theory, treating the human body as a dynamic system of multiple interacting subsystems. It simulates the dynamic interaction and long-term cumulative effects of multiple factors, identifies key feedback loops, evaluates positive and negative feedback regulation mechanisms, and more accurately predicts the development trend of health risks. An intervention strategy engine based on agent simulation creates virtual "digital agents" for the elderly, simulating the impact of different intervention measures on the health evolution path of the "digital agent," evaluating and selecting the optimal strategy, and generating personalized health management plans and lifestyle guidance suggestions to improve the targeting and effectiveness of interventions. A state monitoring system based on Hidden Markov Models detects health state transition patterns and intervention effect feedback. Based on real-time collected physical examination data and initial health management plans, it dynamically updates early warning information and optimizes and adjusts health management plans, achieving continuous adaptive optimization of health management strategies.

[0059] Please see Figure 2 Another embodiment of the multimodal AI model method for health checkups of the elderly in this invention includes: 201. Based on dynamic system theory, a unified model of physiological state is constructed by inputting the collected electrocardiogram signals, B-ultrasound images, and laboratory test data into a physiological system dynamic model based on the principle of continuous medium mechanics. By simulating the stress-strain relationship and energy transfer process of the human physiological system, a physiological state tensor that uniformly represents the comprehensive physiological state of the elderly is generated. Specifically, using electrocardiogram signal characteristic parameters, ultrasound image structural parameters, and biochemical index parameters from laboratory reports as inputs, a physiological parameter correlation network reflecting the interaction relationships between various physiological parameters is constructed based on the constitutive relation theory in continuum mechanics, generating a physiological system network model with a topological structure. Based on the node connections in the physiological system network model, the stress transmission paths and strain response characteristics between various physiological subsystems are analyzed using elasticity theory, generating a stress distribution matrix reflecting the internal mechanical state of the system. Based on the stress distribution matrix and the principle of thermodynamic energy conservation, the energy flow and transformation processes of the cardiovascular, metabolic, and immune systems are simulated, generating an energy state vector characterizing the system's energy state. Based on the energy state vector and real-time acquired physiological time-series data, an evolution equation for the physiological state is established based on dynamic system theory. By solving for the system's equilibrium state and dynamic response, a physiological state tensor that uniformly represents the comprehensive physiological state of the elderly is generated. The physiological state tensor is compared and verified with normal physiological range parameters in a medical knowledge base. Based on the principle of tolerance analysis, the rationality and consistency of the tensor data are quality controlled, generating the final physiological state tensor output that has passed quality control verification.

[0060] It should be noted that a hypothetical elderly patient is used as an example to illustrate the process from multimodal data input to the generation of a physiological state tensor. A 70-year-old male patient had the following data collected during a health check-up: Electrocardiogram signal characteristics: heart rate 75 bpm, PR interval 160 ms, QTc interval 420 ms. Ultrasound imaging structural parameters: left ventricular end-diastolic diameter 50 mm, interventricular septum thickness 12 mm, aortic valve flow velocity 1.8 m / s. Laboratory biochemical parameters: fasting blood glucose 6.1 mmol / L, total cholesterol 5.2 mmol / L, creatinine 90 μmol / L.

[0061] Based on constitutive relation theory in continuum mechanics, a physiological parameter correlation network is constructed. The aforementioned parameters are used as network nodes, with heart rate, left ventricular diameter, and blood glucose as key nodes. According to medical knowledge, the interaction relationships between nodes are defined: heart rate and left ventricular diameter are correlated through the Frank-Starling mechanism, and blood glucose and cholesterol are coupled through metabolic pathways. Weighted edges are used to represent the correlation strength, with the weight of the heart rate-left ventricular diameter edge being 0.7 (based on clinical experience). This generates a network model with a topological structure, where nodes represent physiological parameters and edges represent interactions.

[0062] Based on the connectivity in the network model, stress transmission is analyzed using elasticity theory. Starting from the left ventricular node, stress is transmitted to the vascular node through myocardial stiffness parameters, simulating a stress distribution matrix. Matrix elements represent stress values ​​between subsystems; the stress of the cardiovascular subsystem on the metabolic subsystem is 0.5 kPa, and the stress of the immune subsystem on the cardiovascular subsystem is 0.3 kPa, reflecting the internal mechanical state of the system.

[0063] Energy flow is simulated using a stress distribution matrix. Stress values ​​are converted into energy flow, with the cardiovascular subsystem receiving 100 joules of energy input, the metabolic subsystem consuming 60 joules, and the immune subsystem storing 20 joules. An energy state vector is generated, with each element representing the net energy value of its respective subsystem.

[0064] Based on real-time physiological time-series data (continuously monitored heart rate variability), an evolutionary equation was established using dynamic system theory. The equation describes the change of the energy vector over time, and the equilibrium state was obtained through numerical solution (Euler method): the system stabilizes near the energy vector [95, 58, 18] joules. Dynamic response analysis shows that under external disturbances (slight motion), the system can recover equilibrium within 10 seconds, thus generating a physiological state tensor, a 3×3 matrix. Rows represent the cardiovascular, metabolic, and immune subsystems, and columns represent the energy, stress, and strain dimensions. The first row [95, 0.5, 0.02] represents the state of the cardiovascular subsystem.

[0065] The tensor data was compared with the normal range in the medical knowledge base (the normal energy range for the elderly is 80-120 joules) and tolerance analysis was performed. The tensor data deviation was within 5%, passed quality control verification, and the final physiological state tensor was output.

[0066] 202. Multimodal data semantic association and feature fusion: Based on the physiological state tensor input, a semantic association engine constructed based on fuzzy logic and grey system theory is established to establish the mapping relationship between different modal medical data. Through small sample trend extraction and uncertainty processing, an association feature matrix that integrates multi-source information is generated. Specifically, based on the physiological state tensor and fuzzy set theory, a semantic membership function is constructed to establish a fuzzy mapping relationship between electrocardiogram features, ultrasound image features, laboratory indicators, and clinical semantic concepts, generating a multimodal semantic mapping rule set. Based on the multimodal semantic mapping rule set and real-time acquired medical data, the grey correlation degree between each modality's data sequences is calculated using grey system theory, generating a correlation degree tensor reflecting the intrinsic connections between data. Based on the correlation degree tensor and grey prediction theory, trend mining and feature enhancement are performed on finite sample data to generate a trend feature vector containing long-term trends. Based on the trend feature vector and the semantic mapping rule set, uncertainty and contradictory information in the data are processed using a fuzzy inference system, generating a fusion feature set that has passed consistency verification. Based on the fusion feature set and matrix analysis theory, a multimodal feature association structure is constructed, and semantic rationality is verified through a medical knowledge base to generate the final multi-source information association feature matrix.

[0067] It should be noted that the input is the physiological state tensor from step 201, a 3×3 matrix containing the energy and mechanical states of the cardiovascular, metabolic, and immune subsystems. Simultaneously, its latest real-time data is input: heart rate 78 bpm, left ventricular diameter 50.2 mm, and fasting blood glucose 6.3 mmol / L.

[0068] Based on fuzzy set theory, membership functions are defined for clinical semantic concepts ("compensatory period of cardiac function", "critical glucose metabolism"). A heart rate of 78 beats / min, according to the fuzzy set "normal heart rate", has a membership degree of 0.7 (close to the upper limit of normal); a fasting blood glucose of 6.3 mmol / L belongs to the fuzzy set "high blood glucose" with a membership degree of 0.8. From this, the rule is generated: "IF left ventricular diameter larger AND heart rate faster THEEN increased cardiac workload (confidence 0.85)".

[0069] Based on the above rules and the patient's time-series data for the past three months (monthly blood glucose records: 6.0, 6.1, 6.3 mmol / L), grey system theory was used to analyze the correlation between different modal data sequences. The calculation revealed a grey correlation coefficient of 0.75 between the blood glucose change sequence and the left ventricular diameter change sequence, indicating a strong intrinsic link between metabolic indicators and cardiac structural parameters. This generated a correlation tensor, quantifying the correlation strength between different parameters.

[0070] Due to the limited data points (small sample size) of only three months, grey prediction theory was applied to predict blood glucose trends. The prediction showed that blood glucose levels might rise to 6.6 mmol / L in the next three months. Based on this, a trend feature vector was generated, containing both the current value and the predicted trend information.

[0071] The trend feature vector was input into the fuzzy inference system. The system found that the trend of "continuously rising blood sugar" was consistent with the semantic rule of "increased cardiac workload," but did not contradict the rule of "normal blood lipids." After processing, the uncertainty of short-term fluctuations was eliminated, a consistent fusion feature set was generated, and the core feature of "abnormal metabolic-cardiovascular correlation" was confirmed.

[0072] The fused feature set was organized into three dimensions: cardiovascular, metabolic, and immune. A 3×3 association feature matrix was constructed based on matrix theory. Rows represent physiological systems, and columns represent feature dimensions such as structure, function, and trends. Matrix elements were filled with the associated feature values, with the value at the cardiovascular-functional intersection being "increased cardiac workload (confidence 0.82)". Finally, the semantic rationality was verified using a medical knowledge base, confirming that this association between blood glucose and cardiac function is common in clinical practice, thus generating the final multi-source information association feature matrix.

[0073] 203. System dynamics health risk prediction: Input the correlation feature matrix into a health risk assessment model based on system feedback theory, and generate a quantitative health risk index and early warning signal for chronic diseases by simulating the dynamic interaction and long-term cumulative effect of multiple factors. Specifically, based on the correlation feature matrix and system dynamics theory, a multi-factor interaction network including physiological parameters, environmental factors, and lifestyle habits is constructed. Positive and negative feedback loops are established to simulate the dynamic interactions between factors, generating a system state evolution vector. Based on this vector, the long-term cumulative effect of chronic disease risk factors is analyzed using the integrator principle in control theory. The cumulative exposure of each risk factor is calculated through time-series integration, generating a risk cumulative effect field. Based on this field, network flow theory is used to identify the transmission paths and key nodes of risk in the physiological system. Path sensitivity analysis is used to determine the main risk propagation channels, generating a risk transmission path map. Using the risk transmission path map and real-time monitored physiological parameters as input, multi-scale system theory is used to assess organ-level, system-level, and overall health risk levels, generating a hierarchical risk quantification index set. Based on this hierarchical risk quantification index set and signal detection theory, abnormal patterns exceeding thresholds are identified. Clinical rationality is verified using a medical knowledge base, generating the final quantitative health risk index and early warning signals for chronic diseases.

[0074] It should be noted that the input is the multi-source information association feature matrix from step 202. This matrix contains the fused features: cardiovascular load status (value 0.82, indicating a heavy load), glucose metabolism trend (value 0.75, showing an upward trend), and the correlation strength between these features.

[0075] Based on the correlation feature matrix, a network is constructed that includes physiological parameters (blood glucose, blood pressure), environmental factors (sedentary time), and lifestyle habits (daily sodium intake). Feedback loops are established between nodes: "elevated blood glucose" -> "worsened insulin resistance" constitutes a positive feedback loop (self-reinforcing); while "regular exercise" -> "increased insulin sensitivity" -> "decreased blood glucose" constitutes a negative feedback loop (maintaining stability). By simulating these dynamic interactions, a system state evolution vector is generated. .

[0076] Based on the integrator principle of control theory, risk factors are integrated over time. The patient's average annual blood glucose level over the past 5 years is set at 6.0 mmol / L, and the difference exceeding the ideal range (5.6 mmol / L) is defined as ΔC. The cumulative exposure (CE) can be approximated as follows: With an annual average ΔC of 0.4 mmol / L, the five-year cumulative exposure... mmol / L·year. Similarly, other factors (cumulative exposure to hypertension) are calculated to generate a "risk cumulative effect field" that includes the cumulative amount of each risk factor.

[0077] Based on network flow theory, risk transmission pathways were identified within the "risk accumulation effect field." Analysis revealed that accumulated hyperglycemia is the primary source, with its risk flow preferentially transmitted to the cardiovascular system via the "metabolism-inflammation" pathway, which exhibits the highest sensitivity (60% contribution). Conversely, the "metabolism-renal" pathway shows lower sensitivity (20% contribution). This resulted in the generation of a map identifying "insulin resistance" and "vascular endothelial function" as key nodes.

[0078] The risk transmission pathway was combined with real-time monitoring data (current blood pressure 145 / 90 mmHg) for multi-scale assessment: Organ level: Cardiac myocardial ischemia risk index was 0.65 (threshold 0.7), and renal function decline risk index was 0.45 (threshold 0.6). System level: Cardiovascular system comprehensive risk index was 0.72 (exceeding the threshold of 0.7), and metabolic system risk index was 0.68. Overall level: Overall health risk index was 0.70.

[0079] Based on hierarchical indicators and signal detection theory, the "Comprehensive Cardiovascular Risk Index" of 0.72 was identified as exceeding the threshold and showing a continuously rising pattern. Verification using a medical knowledge base confirmed that this pattern aligns with the early clinical trajectory of cardiovascular complications in type 2 diabetes. The final quantitative health risk index was 0.70 (belonging to the moderate-to-high risk group), and an early warning signal for chronic diseases was issued: "Be alert to the risk of cardiovascular complications arising from type 2 diabetes."

[0080] 204. Personalized health intervention program generation: Based on the health risk index and early warning signals, the intervention strategy engine based on agent simulation, combined with the individual characteristic parameters of the elderly, generates targeted health management programs and personalized lifestyle guidance suggestions by simulating the behavioral impact paths of different intervention measures. Specifically, based on health risk indices, early warning signals, and basic information and lifestyle data of the elderly as inputs, a virtual agent model reflecting individual physiological characteristics and behavioral patterns is constructed based on agent modeling theory, generating an individual agent model with personalized attributes. Based on the individual agent model and various health intervention programs in the intervention program library, the behavioral response paths of different intervention programs on the individual agent model are simulated based on complex systems theory, generating an intervention behavior influence network diagram. Based on the intervention behavior influence network diagram, the comprehensive effects of each intervention program are evaluated from three dimensions—compliance, effectiveness, and safety—based on multi-objective optimization theory, generating an optimized sequence of intervention programs. Using the optimized sequence of intervention programs and the characteristic parameters of the individual agent model as inputs, the most suitable intervention combination for the current individual characteristics of the elderly is selected based on fitness analysis theory, generating a preliminary personalized health management plan. Based on the preliminary personalized health management plan and the elderly's living environment, economic conditions, and other realistic constraints, the plan is adjusted for real-world adaptability based on feasibility analysis theory, generating a final targeted health management plan and specific, actionable lifestyle guidance suggestions.

[0081] It should be noted that the health risk index (0.70, moderate to high risk) and warning signal (be alert for cardiovascular complications caused by type 2 diabetes) from step 203 are relevant. Combined with the patient's individual characteristics: 70 years old, living alone, long-term smoking history (quit 5 years ago), preference for salty food, low level of physical activity, and average economic conditions.

[0082] Based on agent modeling theory, a virtual "patient agent" is created. This model encapsulates the patient's physiological characteristics (low insulin sensitivity, decreased vascular elasticity) and behavioral patterns (3000 steps per day, approximately 5 grams of sodium intake per day). The model can simulate the patient's possible responses to different interventions.

[0083] The intervention program selects options from a library of interventions: "150 minutes of moderate-intensity walking per week," "daily sodium intake controlled to <2 grams," and "attending community health lectures." Simulations on an individual agent model show that implementing the "walking program" directly affects "blood sugar" by "improving insulin sensitivity," thereby indirectly reducing "cardiovascular load," forming a positive impact pathway. However, the model also simulates that "mild osteoarthritis of the knee" may become an obstacle to maintaining exercise. This generates a network diagram showing the multi-level impact pathways of each program.

[0084] Based on network diagrams, each treatment plan is evaluated from three dimensions: adherence (patients' ability to persist), effectiveness (the degree of improvement in health indicators), and safety (avoidance of risks such as sports injuries). To this end, we propose a comprehensive benefit index formula for evaluation: Evaluation of the "sodium reduction diet" program: effectiveness score 0.8, adherence probability 0.6 (due to difficulty in changing taste habits), and extremely low safety risk coefficient of 0.1. The assessment of "water exercise" showed an effectiveness score of 0.7, a compliance rate of 0.8 (joint-friendly), and a safety risk coefficient of 0.05. Therefore, "water exercise" is ranked higher than "sodium reduction in diet". The final optimized sequence is: 1. Water exercise, 2. Dietary adjustments (first reduce salt to 3.5 grams / day), 3. Regular blood pressure monitoring.

[0085] The sorted sequence of solutions was combined with the characteristics of the surrogate model. Considering that the patient lives alone and has relatively good mobility, "water exercise in the community swimming pool 3 times a week" and "gradually reducing salt intake using a salt-limiting spoon" were selected as the core combination to generate a preliminary plan.

[0086] Initial assessment: The community swimming pool membership fee is 300 yuan per month, which is affordable for the patient; however, the pool is 1.5 kilometers from home, making transportation slightly inconvenient. Based on the feasibility analysis, a more realistic plan was adjusted: The final health management plan is as follows: 1. Exercise guidance: Every Monday, Wednesday, and Friday, the patient will be accompanied by family members to the community center by public transportation for water exercise (45 minutes / session); every Tuesday, Thursday, and Saturday, the patient will perform chair yoga at home (15 minutes / session). 2. Dietary recommendations: Immediately use a salt-limiting spoon, aiming to reduce the daily salt intake from 5 grams to 3.5 grams within 3 months, specifically providing practical methods for "using spices to replace some of the salt for seasoning".

[0087] 205. Dynamic monitoring and program optimization: Based on the health management program and real-time collected physical examination data, the system inputs them into a state monitoring system based on a hidden Markov model. By detecting health status transition patterns and intervention effect feedback, the system generates dynamically updated early warning information and optimized health management programs, thereby achieving continuous adaptive optimization of health management strategies.

[0088] Specifically, based on the health management plan and real-time collected physical examination data, a health state transition probability model is constructed using Hidden Markov State Theory. The transition patterns of health states are identified through observation sequence analysis, generating a health state transition probability matrix. Based on this matrix, a three-tiered monitoring network comprising physiological indicators, functional states, and overall health is constructed using system monitoring theory, generating a hierarchically related health state monitoring network. The health state monitoring network is then combined with the effect data after implementing the health management plan. Based on feedback mechanisms in control theory, the actual effects of intervention measures are analyzed, generating an intervention effect feedback vector containing effect evaluation indicators. Based on this feedback vector, the original warning signals are recalibrated using signal update theory, and an updated dynamic warning information set is generated through a threshold adaptive adjustment mechanism. Finally, based on the dynamic warning information set and the health state transition probability matrix, the impact of different adjustment schemes on health state transitions is analyzed using strategy optimization theory, generating an optimized health management plan and feeding it back to the intervention strategy engine.

[0089] It should be noted that dynamic monitoring and treatment plan optimization were implemented for this 70-year-old male patient.

[0090] Input: Personalized health management plan from step 204 (3 water exercises per week, daily salt restriction) and real-time physical examination data (follow-up data one month later: blood pressure 142 / 88 mmHg, fasting blood glucose 6.2 mmol / L, patient reported exercise plan completion rate of 70%).

[0091] Based on Hidden Markov State Theory, three health states are defined: "Stable," "Critical," and "Risk." By analyzing a one-month observational data sequence (weekly data of "blood pressure-blood sugar-exercise completion rate"), the transition probabilities between states are calculated. It is found that when the "exercise completion rate" is above 80%, the probability of transitioning from the "critical" state back to the "stable" state is 0.7; while when the "completion rate" is below 60%, the probability of transitioning from the "critical" state to the "risk" state increases to 0.6. This generates a state transition probability matrix, quantifying the likelihood of changes in health state.

[0092] Based on the matrix above, a hierarchical monitoring network is established: Physiological Indicators Layer: Directly monitors blood pressure and blood glucose levels. Functional Status Layer: Assesses exercise endurance (6-minute walking distance). Overall Health Layer: Comprehensively assesses quality of life scale scores. Correspondence is established between networks; abnormal blood pressure triggers closer monitoring at the Functional Status Layer.

[0093] By combining network monitoring data with the implementation of the health management program, and analyzing the effects based on a feedback mechanism, the implementation data (completion rate of 70%) and the improvement of physiological indicators (a slight decrease in blood pressure) of the "water exercise" program in the program were comprehensively evaluated to generate an effect feedback vector. The components include: exercise compliance score (0.7), blood pressure control effect score (0.6), and blood glucose control effect score (0.5). This indicates that the exercise program has preliminary effects, but compliance and its impact on blood glucose need to be strengthened.

[0094] Based on the effect feedback vector, the warning signal is recalibrated using signal update theory. The original warning signal was "high risk," but according to the actual effect, the risk increase trend was slightly slower than expected. Therefore, the system uses an adaptive adjustment mechanism to fine-tune the warning level to "medium-high risk" and generates updated dynamic warning information: "Cardiovascular risk persists, and exercise compliance is the key control point."

[0095] Based on the new set of early warning information and the state transition probability matrix, strategy optimization theory analyzes the impact of different adjustment plans. The analysis reveals that adjusting "3 times a week of water exercise" to "2 times a week of water exercise + 2 times a week of home-based assisted cycling training" (considering the patient's report of inconvenience in going to the pool) significantly improves adherence probability, thereby increasing the likelihood of transitioning to a "stable" state. Therefore, the system generates an optimized plan: Exercise adjustment: Water exercise on Tuesdays and Thursdays, and assisted cycling training at home on Saturdays and Sundays (30 minutes each time). Enhanced monitoring: Add one more morning home blood pressure measurement per week. This new plan will be fed back to the intervention strategy engine to initiate the next cycle.

[0096] 206. Based on the health management plan and data on equipment configuration, personnel qualifications, and service capabilities in the regional medical resource database, analyze the resource conditions required for plan implementation based on resource matching theory, and generate a medical resource suitability assessment report. Based on the medical resource suitability assessment report, optimize the balance between the three objectives of optimal effect, lowest cost, and highest feasibility based on multi-objective decision-making theory, and generate a personalized health management implementation path planning map. Based on the implementation path planning map, identify potential obstacles and risks during plan implementation based on risk matrix theory, and generate a risk prevention and control plan set including contingency plans. Based on the risk prevention and control plan set and organizational structure data of primary healthcare institutions, construct a cross-departmental and cross-level collaborative network for plan execution based on collaborative network theory, and generate a collaborative execution network diagram with clear task allocation. Based on process quality control theory, construct an effect tracking system by combining the collaborative execution network diagram and execution log data during plan implementation, and generate a health management effect assessment report including quality indicators.

[0097] It should be noted that the resource matching and implementation plan for the health management program for this 70-year-old male patient was as follows. Input: The personalized health management plan from step 204 (twice a week of community water exercise + twice a week of home-assisted bicycle training, dietary guidance, and regular monitoring) and the regional medical resource database (data from the health center, sports center, and tertiary hospital in the local community).

[0098] Based on resource matching theory, the compatibility of the required resources with existing resources is analyzed: Equipment configuration: The plan requires a heated swimming pool and heart rate monitoring equipment. The community sports center has a swimming pool (100% match), but the health center only has a blood pressure monitor, not an electrocardiograph (ECG) (60% match). Personnel qualifications: The plan requires a sports rehabilitation therapist and a nutritionist. The community health center has one general practitioner who can also provide nutrition consultation (70% match), but no dedicated sports rehabilitation therapist (0% match), requiring reliance on the sports center's coaches. Service capacity: The sports center's swimming lanes are saturated during peak hours and can only provide services to this patient during weekday afternoons (80% match). The generated report points out that the key gap lies in the lack of a dedicated sports rehabilitation therapist in the community, while the advantage lies in the basic availability of infrastructure.

[0099] Based on multi-objective decision-making theory, this involves a trade-off between effectiveness, cost, and feasibility. We propose a formula for quantitative evaluation: ; Where E represents the expected effect (0-1), C represents the estimated cost, Cmax represents the maximum acceptable cost (monthly expenditure of 500 yuan), and F represents the feasibility (0-1).

[0100] Path A (completely dependent on top-tier hospitals): E=0.9 (best results), but C=800 yuan (exceeds budget), F=0.3 (long distance, difficult to get an appointment). .

[0101] Route B (primarily community-based, with hospital referrals as a backup): E=0.7, C=200 yuan (transportation and minor expenses only), F=0.9. .

[0102] Pathway B received the highest score, therefore the planning map was determined as follows: with community health centers and sports centers as the main implementing bodies, a green referral channel was established with tertiary hospitals for emergency use.

[0103] Based on the planning map and risk matrix theory, risk points are identified as follows: High risk (high probability, significant impact): Patient falls during exercise. Contingency plan: Choose water-based exercises with buoyancy, with family members initially accompanying the patient. Medium risk (medium probability, moderate impact): The sports center swimming pool is temporarily closed for maintenance. Contingency plan: Obtain the schedule in advance; the family exercise program will automatically activate during the closure period.

[0104] Based on collaborative network theory, the task allocation is clearly defined: Community health center general practitioners: overall responsible, monthly follow-up assessments. Sports center coaches: guide water sports, record participation. Patient family members: assist with transportation, supervise family exercise implementation. Tertiary hospital cardiology department: receive referrals, handle abnormal situations. The network diagram clearly defines the information flow (coaches providing feedback on participation to doctors) and the chain of responsibility.

[0105] Three months after the implementation of the program, based on process quality control theory, combined with execution logs (actual exercise attendance rate of 85%) and physical examination data (blood pressure dropped to 138 / 85 mmHg), an evaluation report was generated, which included quality indicators such as "program implementation compliance" and "physiological indicator improvement rate", proving that the path was effective.

[0106] In this embodiment of the invention, a unified physiological state model is performed based on dynamic systems theory to simulate the complex mechanical and energy processes of the human physiological system. The generated physiological state tensor can dynamically and comprehensively represent the physiological state of the elderly. Compared with static assessment methods, it can better reflect the real-time changes in the elderly's body and improve the accuracy of health assessment. A multi-factor interaction network is constructed using system dynamics theory to simulate the dynamic interactions between various factors, which can more accurately predict the health risks of the elderly and generate quantitative health risk indices and early warning signals for chronic diseases. This helps to detect potential health problems early and provides a basis for personalized intervention. Based on the health risk index and warning signals, combined with the individual characteristic parameters of the elderly, targeted health management plans and personalized lifestyle guidance suggestions are generated through surrogate simulation technology. The physiological characteristics, behavioral patterns, living habits, and realistic constraints of the elderly are considered, making the intervention plan more operable and effective, and improving the elderly's adherence to health management. A state monitoring system is constructed based on a hidden Markov model, which can detect the transition patterns of the elderly's health status and the feedback of intervention effects in real time. By dynamically updating early warning information and optimizing health management plans, continuous adaptive optimization of health management strategies is achieved, ensuring that health management plans always align with the actual health conditions of the elderly and improving the effectiveness of health management. Based on the health management plan and regional medical resource database, and using resource matching theory and multi-objective decision-making theory, the resource conditions required for plan implementation are analyzed. A trade-off optimization is made among the three objectives of optimal effect, lowest cost, and highest feasibility, generating a personalized health management implementation path planning map. This helps to rationally utilize medical resources, improve resource utilization efficiency, and reduce health management costs. Based on risk matrix theory, potential obstacles and risks encountered during plan implementation are identified, generating a risk prevention and control plan set including contingency plans. Based on collaborative network theory, a cross-departmental and cross-level collaborative network for plan execution is constructed, clearly defining task allocation. This effectively addresses various risks in the health management process, ensuring smooth plan implementation and improving the overall effectiveness of health management.

[0107] Figure 3 This is a schematic diagram of a multimodal AI model device for health checkups of the elderly, provided by an embodiment of the present invention. The device 300 can vary significantly due to different configurations or performance, and may include one or more central processing units (CPUs) 310 (e.g., one or more processors) and a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) for storing application programs 333 or data 332. The memory 320 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the device 300. Furthermore, the processor 310 may be configured to communicate with the storage media 330 and execute the series of instruction operations in the storage media 330 on the device 300.

[0108] Device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 3 The multimodal AI model device structure shown for health checkups of the elderly does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0109] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the multimodal AI model for health checkups of the elderly.

[0110] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0111] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0112] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multimodal AI model for health checkups of the elderly, characterized in that, include: The physiological module is used to input the collected electrocardiogram signals, B-ultrasound images, and laboratory test data into the physiological system dynamics model, and generate physiological state tensors by simulating the stress-strain relationship and energy transfer process of the human physiological system. The mapping module is used to establish mapping relationships between different modalities of medical data based on the physiological state tensor, and generate an association feature matrix through small sample trend extraction and uncertainty processing. The allocation module is used to input the correlation feature matrix into the health risk assessment model, and generate a health risk index and early warning signals for chronic diseases by simulating the dynamic interaction and long-term cumulative effect of multiple factors. The intervention module is used to input the health risk index and early warning signals of chronic diseases into the intervention strategy engine, and combine them with the individual characteristic parameters of the elderly to generate health management plans and lifestyle guidance suggestions by simulating the behavioral impact paths of different intervention measures. The early warning module is used to generate dynamically updated early warning information and adjusted health management plans based on the health management plan and real-time collected physical examination data, by detecting the health status transition pattern and intervention effect feedback.

2. The multimodal AI model for health checkups of the elderly according to claim 1, characterized in that, include: Based on the characteristic parameters of electrocardiogram signals, structural parameters of B-ultrasound images, and biochemical parameters of laboratory reports, and based on the constitutive relation theory in continuum mechanics, a physiological parameter correlation network reflecting the interaction between various physiological parameters is constructed to generate a physiological system network model. Based on the node connection relationships in the physiological system network model, the stress transmission path and strain response characteristics between each physiological subsystem are analyzed to generate a stress distribution matrix; Based on the stress distribution matrix and the thermodynamic energy conservation principle, the energy flow and transformation processes of the cardiovascular, metabolic, and immune systems are simulated to generate an energy state vector characterizing the energy state of the system. Based on the energy state vector and real-time collected physiological time-series data, an evolution equation for the physiological state is established. By solving the equilibrium state and dynamic response of the system, a physiological state tensor is generated. The physiological state tensor is compared and verified with the normal physiological range parameters in the medical knowledge base. The rationality and consistency of the tensor data are quality controlled, and a quality-controlled and verified physiological state tensor is generated.

3. The multimodal AI model for health checkups of the elderly according to claim 1, characterized in that, include: Based on the physiological state tensor and fuzzy set theory, a semantic membership function is constructed to establish a fuzzy mapping relationship between electrocardiogram features, B-ultrasound image features, laboratory indicators and clinical semantic concepts, and a multimodal semantic mapping rule set is generated. Based on the multimodal semantic mapping rule set and real-time acquired medical data, the grey relational degree between data sequences of each modality is calculated, and a relational degree tensor is generated; Based on the aforementioned correlation tensor, trend mining and feature enhancement are performed on a limited sample of data to generate trend feature vectors; Based on trend feature vectors and semantic mapping rule sets, uncertainties and contradictions in the data are processed to generate a fused feature set; A multimodal feature association structure is constructed based on the fused feature set, and semantic rationality is verified through a medical knowledge base to generate an association feature matrix.

4. The multimodal AI model for health checkups of the elderly according to claim 3, characterized in that, include: Based on the correlation feature matrix and the theory of system dynamics, a multi-factor interaction network is constructed. By establishing positive and negative feedback loops, the dynamic interaction between various factors is simulated, and the system state evolution vector is generated. Based on the system state evolution vector, the long-term cumulative effect of chronic disease risk factors is analyzed, the cumulative exposure of each risk factor is calculated, and a risk cumulative effect field is generated. Based on the risk accumulation effect field, identify the transmission path and key nodes of risk in the physiological system, determine the main risk propagation channels, and generate a risk transmission path map; By using risk transmission pathway maps and real-time monitored physiological parameters as inputs, the health risk levels at the organ level, system level, and overall level are assessed respectively, generating a hierarchical set of risk quantification indicators; Based on a hierarchical risk quantification index set, abnormal patterns exceeding the threshold are identified, and clinical rationality is verified by combining a medical knowledge base to generate a health risk index and early warning signals for chronic diseases.

5. The multimodal AI model for health checkups of the elderly according to claim 4, characterized in that, include: Based on health risk index, early warning signals, and basic information and lifestyle data of the elderly as input, a virtual agent model reflecting individual physiological characteristics and behavioral patterns is constructed to generate an individual agent model; Based on the individual agent model and various health intervention programs in the intervention measure library, the behavioral response paths of different intervention measures on the individual agent model are simulated to generate a network diagram of the influence of intervention measures on behavior. Based on the behavioral impact network diagram of intervention measures, the comprehensive effect of each intervention program is evaluated from three dimensions: compliance, effectiveness, and safety, and an optimized sequence of intervention programs is generated. By taking the optimized sequence of intervention programs and the characteristic parameters of the individual agent model as input, the most suitable combination of interventions for the current individual characteristics of the elderly is selected to generate a preliminary personalized health management plan. Based on the initial personalized health management plan and the actual constraints of the elderly, the plan is adjusted to adapt to the actual situation, and a health management plan and lifestyle guidance suggestions are generated.

6. The multimodal AI model for health checkups of the elderly according to claim 5, characterized in that, The overall effectiveness of each intervention program was evaluated from three dimensions: adherence, effectiveness, and safety. : 。 7. The multimodal AI model for health checkups of the elderly according to claim 5, characterized in that, include: Based on the aforementioned health management plan and real-time collected physical examination data, a health status transition probability model is constructed. By analyzing the observation sequence, the transition patterns of health status are identified, and a health status transition probability matrix is ​​generated. Based on the aforementioned health state transition probability matrix, a three-level monitoring network comprising a physiological indicator layer, a functional state layer, and an overall health layer is constructed based on system monitoring theory to generate a health state monitoring network. By combining the health status monitoring network with the effect data after the implementation of the health management program, we can analyze the actual effect of the intervention measures and generate an intervention effect feedback vector. Based on the intervention effect feedback vector, the original warning signal is recalibrated, and an updated dynamic warning information set is generated through a threshold adaptive adjustment mechanism. Based on the dynamic early warning information set and the health status transition probability matrix, the impact of different adjustment schemes on health status transition is analyzed, and an optimized health management scheme is generated.

8. The multimodal AI model for health checkups of the elderly according to claim 1, characterized in that, It also includes an evaluation module: Based on the health management plan and data on equipment configuration, personnel qualifications, and service capabilities in the regional medical resource database, analyze the resource conditions required for the implementation of the plan and generate a medical resource suitability assessment report. Based on the aforementioned medical resource suitability assessment report, a balance is struck and optimized among the three objectives of optimal effect, lowest cost, and highest feasibility to generate a personalized health management implementation path planning map; Based on the implementation path planning map, identify the obstacles and risks that may be encountered during the implementation of the plan, and generate a set of risk prevention and control plans that include contingency plans; Based on the risk prevention and control plan set and the organizational structure data of primary medical institutions, a cross-departmental and cross-level plan execution collaboration network is constructed, and a collaboration execution network diagram is generated; By combining the collaborative execution network diagram with the execution log data during the implementation process, an effect tracking system is built to generate a health management effect evaluation report.

9. The multimodal AI model for health checkups of the elderly according to claim 8, characterized in that, The optimization involves balancing and optimizing among the three objectives: optimal effectiveness, lowest cost, and highest feasibility. for : Where E represents the expected result and C represents the estimated cost. F represents the maximum acceptable cost and the feasibility factor.

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