An intelligent dynamic adaptive post-lung transplantation rehabilitation management platform and method

Abstract

The present application relates to the technical field of intelligent medical treatment and health management, in particular to an intelligent dynamic adaptive lung transplantation postoperative rehabilitation management platform and method. It comprises a multi-source data sensing and fusion module, a dynamic risk assessment and decision module, a personalized intervention execution and feedback module, and a closed-loop learning optimization module. The platform generates panoramic state data by collecting multi-modal heterogeneous data of patients; performs real-time risk analysis using a first artificial intelligence model, outputs and predicts complications; predicts rehabilitation needs through a second artificial intelligence model; generates dynamically adaptive rehabilitation intervention instructions in combination with a clinical knowledge base; executes and collects feedback data through a user terminal; and finally optimizes model parameters in a closed loop using feedback data. The present application realizes intelligent, dynamic personalized and continuous self-optimization of lung transplantation postoperative rehabilitation management.

Description

Technical Field

[0001] This invention relates to the field of smart healthcare and health management technology, and more specifically, to an intelligent, dynamically adaptable post-lung transplant rehabilitation management platform and method. Background Technology

[0002] Lung transplantation is the ultimate treatment for end-stage lung disease, but its postoperative management is extremely complex and challenging. Due to factors such as direct exposure of the lungs to the external environment, persistent immunosuppression after transplantation, and ischemia-reperfusion injury, the incidence of postoperative complications (such as acute rejection, infection, primary transplant dysfunction, and airway complications) in lung transplantation is much higher than in other solid organ transplants, and patients have the lowest long-term survival rate.

[0003] Successful post-lung transplant rehabilitation is a complex systemic project involving respiratory support, circulatory management, infection control, immunosuppression, nutritional support, psychological intervention, and multi-stage exercise rehabilitation. It highly relies on meticulous collaboration and intensive monitoring by a multidisciplinary team. Currently, clinical practice mainly follows written specialist nursing routines and rehabilitation pathways (such as ultra-early rehabilitation programs). While these programs are systematic, they have significant limitations in implementation. Therefore, this paper proposes an intelligent, dynamically adaptable post-lung transplant rehabilitation management platform and method.

[0004] The existing technology has the following technical defects, specifically: 1. In existing technologies, patients' vital signs, test results, imaging data, subjective feelings, and environmental data are scattered across heterogeneous systems, resembling "data fragments." Clinical decision-making relies on intensive manual review, memorization, and piecing together by medical staff, failing to create a comprehensive, dynamic, spatiotemporally aligned profile. This fragmented state leads to risk assessments being based solely on alarms triggered by isolated indicators exceeding thresholds, failing to capture subtle early correlations and synergistic changes between parameters across multiple systems. Essentially, it's a case of "the blind men and the elephant," laying the groundwork for missed reports, false alarms, and delayed decision-making.

[0005] 2. Existing rehabilitation programs are essentially "fixed scripts" based on group experience and timelines (e.g., performing activity Y on day X post-surgery). However, a patient's actual physiological recovery trajectory, complication risk window, drug response, and psychological state are highly individualized and constantly changing. Static programs cannot respond in real time to individual state deviations such as volume fluctuations, increased pain, and early signs of infection, leading to interventions that are either "overly aggressive" and risky, or "underly aggressive" and miss the golden recovery period. This "one-size-fits-all" approach is the main bottleneck of current personalized medicine.

[0006] 3. The existing nursing process is an "open-loop" system: assessment → intervention → reassessment. The quantitative assessment of intervention effectiveness (such as the actual benefits of a breathing exercise) is vague, and feedback information is difficult to structure and return to the decision-making starting point. Adjustments to the plan heavily rely on the collective experience and regular discussions of the healthcare team, failing to form a self-evolving closed loop of "data-driven decision-making → implementation feedback → model optimization → better decision-making." This results in slow improvements in healthcare quality and makes it difficult to scale up and replicate best practices. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent, dynamically adaptable post-lung transplant rehabilitation management platform and method to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention aims to provide an intelligent dynamic adaptive lung transplant postoperative rehabilitation management platform, comprising: a multi-source data perception and fusion module, used to collect multimodal heterogeneous data from the patient end, hospital end and environment end, and to perform spatiotemporal alignment and feature fusion processing on the multimodal heterogeneous data to generate panoramic patient status data.

[0009] The dynamic risk assessment and decision-making module is communicatively coupled with the multi-source data perception and fusion module. It is used to perform real-time risk analysis based on the patient's panoramic status data, using a preset personal dynamic baseline model and a first artificial intelligence model, and output early warning signals for complications including acute rejection and infection.

[0010] Based on the patient's overall status data and historical rehabilitation response data, a second artificial intelligence model is used to predict the future effects of different rehabilitation interventions and generate personalized inferences about current rehabilitation needs.

[0011] Based on the early warning signals and the inferred rehabilitation needs, and combined with a clinical knowledge base, dynamically adapted rehabilitation intervention instructions are generated.

[0012] The personalized intervention execution and feedback module is communicatively coupled with the dynamic risk assessment and decision-making module. It is used to issue and execute the rehabilitation intervention instructions through the user terminal, and to collect execution compliance data and patient status change data after execution, forming a feedback data stream.

[0013] The closed-loop learning optimization module is communicatively coupled to the dynamic risk assessment and decision-making module and the personalized intervention execution and feedback module, respectively, and is used to receive the feedback data stream and to perform parameter optimization and incremental training on the first artificial intelligence model and the second artificial intelligence model.

[0014] As a further improvement to this technical solution, the patient panoramic status data is a multi-dimensional feature vector, which includes at least: a temporal physiological feature sub-vector, derived from the features of waveform analysis and trend extraction of continuously monitored physiological parameters.

[0015] The event-related feature subvectors characterize the temporal correlation strength between patient-reported events, medication events, environmental mutation events, and changes in physiological parameters.

[0016] The individual baseline offset feature subvector quantifies the deviation of each current indicator from the statistical distribution of the individual dynamic baseline model.

[0017] As a further improvement to this technical solution, the dynamic risk assessment and decision engine module further includes: a risk quantification unit, used to calculate a comprehensive risk score and main risk source labels based on the early warning signal.

[0018] The rehabilitation needs quantification unit is used to infer and calculate the urgency score for different rehabilitation dimensions based on the rehabilitation needs.

[0019] The decision rule unit is used to receive the comprehensive risk score, risk source labels and urgency scores of each dimension, and access the clinical knowledge base to generate the rehabilitation intervention instructions according to the preset risk-need matching rules.

[0020] As a further improvement to this technical solution, the rehabilitation intervention instructions include at least: dynamic exercise prescription adjustment instructions, enhanced medication management and follow-up instructions, and personalized education content push instructions.

[0021] As a further improvement to this technical solution, the first artificial intelligence model is a hybrid model combining a recurrent neural network or a temporal convolutional network with an anomaly detection algorithm. It establishes the personal dynamic baseline model by continuously learning the temporal patterns of the patient's physiological parameters. The triggering of the early warning signal is determined based on the deviation of the current data stream from the personal dynamic baseline model and the matching degree of a specific pathological pattern.

[0022] As a further improvement to this technical solution, the second artificial intelligence model is a reinforcement learning model or a causal inference model. Its state space includes the patient's panoramic state data, and its action space includes different types of rehabilitation exercise intensities, drug adjustment suggestions, nutrition plans and educational content. The reward function is comprehensively constructed based on the degree of improvement of physiological indicators, the avoidance of complications and patient compliance collected subsequently.

[0023] As a further improvement to this technical solution, the personalized intervention execution and feedback module includes: a compliance quantification unit and a multi-channel execution unit.

[0024] The compliance quantification unit is used to quantify and score the degree of execution of each rehabilitation intervention instruction through user terminal interaction confirmation, smart pillbox opening records, and correlation analysis of wearable device exercise data.

[0025] A multi-channel execution unit is used to coordinate the issuance of the rehabilitation intervention instructions.

[0026] As a further improvement to this technical solution, the closed-loop learning optimization module optimizes the model by: optimizing the first artificial intelligence model by comparing the finally diagnosed complication event with the model's historical warning records. If the model misses or falsely reports, the model uses the patient's panoramic status data within the time window before and after the event as positive / negative samples for incremental training.

[0027] Optimization of the second artificial intelligence model: The changes in physiological indicators after the patient executes the intervention instructions, as well as the corrections made by medical staff to the instructions, are used together as reward signals or new causal pairs to update the decision-making strategy of the second artificial intelligence model.

[0028] The second aspect of the present invention provides a method for a smart dynamic adaptive lung transplant postoperative rehabilitation management platform, comprising: step S1: collecting multimodal heterogeneous data of the patient, and performing spatiotemporal alignment and feature fusion to generate panoramic status data of the patient.

[0029] Step S2: Based on the patient's panoramic status data and personal dynamic baseline, perform real-time risk analysis using a time-series deep learning model to generate early warning signals for complications.

[0030] Step S3: Based on the patient's panoramic status data and historical rehabilitation response data, infer personalized rehabilitation needs through a prediction model, and generate dynamic rehabilitation intervention instructions by combining the clinical knowledge base.

[0031] Step S4: Send the dynamic rehabilitation intervention command to the patient's terminal, monitor the execution of the command, and obtain compliance and physiological feedback data.

[0032] Step S5: Optimize the temporal deep learning model and the prediction model using the compliance and physiological feedback data to achieve dynamic closed-loop adjustment of rehabilitation management strategies.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention aggregates fragmented data into unified, high-dimensional "patient panoramic status data" through a spatiotemporal alignment and feature fusion model based on an attention mechanism. This is not a simple data accumulation, but rather the generation of a dynamic digital twin that comprehensively reflects multiple dimensions such as physiological temporal patterns, event correlation strength, and individual baseline shifts. This provides a completely new technical path to solve the problem of information silos, giving subsequent intelligent analysis a complete and consistent "cognitive foundation" for the first time, representing a fundamental breakthrough in achieving accurate perception.

[0034] 2. This invention creatively deploys a parallel and collaborative architecture of dual AI models. The first model (such as the TCN-anomaly detection hybrid model) focuses on real-time risk scanning and ultra-early warning based on an individual's dynamic baseline; the second model (such as a reinforcement learning model) focuses on rehabilitation effect prediction and demand inference based on historical responses. The outputs of the two are not isolated, but dynamically synthesized through a built-in clinical knowledge graph and risk-demand matching rule engine, ultimately generating a personalized intervention instruction package with adjustable parameters and safety constraints. This marks a paradigm shift in management strategies from "static execution scripts" to "dynamically generated scripts," truly achieving millisecond-level precise adaptation of intervention measures to the patient's instantaneous state.

[0035] 3. This invention overcomes the limitations of traditional open-loop management by designing a quantitative feedback and closed-loop learning optimization module. It transforms instruction execution compliance, physiological response curves, and even clinical correction records into a structured feedback data stream. Using this feedback, the system can incrementally train the first model based on missed / false alarm events and optimize the second model based on actual benefits (reward signals). This transforms the entire platform from a fixed rule enforcer into a self-evolving intelligent agent capable of learning from every clinical interaction and continuously fine-tuning its warning thresholds and decision-making strategies, fundamentally solving the problem of the difficulty in digitizing and automatically iterating medical experience. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0038] Figure 2 This is a schematic diagram of the implementation steps of the method of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example: Please refer to Figure 1 As shown, an intelligent dynamic adaptive lung transplant postoperative rehabilitation management platform is provided, including: a multi-source data perception and fusion module, used to collect multimodal heterogeneous data from the patient end, hospital end and environment end, and perform spatiotemporal alignment and feature fusion processing on the multimodal heterogeneous data to generate panoramic patient status data.

[0041] In another embodiment, the multi-source data sensing and fusion module specifically includes: The hospital's data unit is used to acquire electronic medical records, medical images, and laboratory test data.

[0042] The wearable device data unit is used to acquire patients' dynamic lung function indicators, blood oxygen saturation, heart rate, and activity level data in real time.

[0043] The environmental data unit is used to acquire air quality, temperature, and humidity data of the patient's environment.

[0044] The Patient Reported Outcomes (PTO) data unit is used to receive data on symptoms, feelings, quality of life, and pain scores proactively reported by patients.

[0045] The nurse input unit is used to receive the following information entered by nurses via mobile terminals or clinical systems: Record the patient's subjective feelings, such as shortness of breath, fatigue, and the location and nature of pain; Symptom observation and record, such as the nature of the cough, the amount / color / viscosity of sputum, and the degree of sweating; Manual verification values ​​of vital signs; Record the implementation of rehabilitation exercises, such as whether the exercises were performed correctly and whether there were any adverse reactions; A brief assessment of the patient's psychological state, such as signs of anxiety and depression; Records of medical orders and reporting of abnormal events.

[0046] The data fusion unit employs an attention-based neural network model to perform spatiotemporal alignment and feature-level fusion of heterogeneous data from the aforementioned units, thereby constructing a unified patient state vector.

[0047] In one specific embodiment, the respiratory pattern features in the patient panoramic status data are extracted and analyzed in the following manner: Breathing patterns include, but are not limited to: Respiratory rate: The number of respiratory cycles per unit of time; Respiratory depth: tidal volume or range of chest and abdominal movement; Breathing rhythm: Is it regular? Are there any abnormal rhythms such as apnea or Cheyne-Stokes respiration? Breath sound characteristics: Breath sound signals collected by electronic stethoscope or wearable device are analyzed to determine the presence or absence of dry and wet rales, wheezing, etc. Breathing effort: Indirectly assessed through chest and abdominal movement coordination and the use of accessory respiratory muscles; Breathing pattern types: such as thoracic breathing, abdominal breathing, mixed breathing and their proportions.

[0048] Breathing pattern features are collected synchronously through the respiratory sensor, chest and abdominal motion sensor and sound sensor of the wearable device. After signal processing, the above multi-dimensional features are extracted to form a breathing pattern feature sub-vector, which is then incorporated into the patient's panoramic status data.

[0049] Pain is an important patient-reported indicator, and its analysis and evaluation process is as follows: Assessment tools: Digital pain assessment scales are used, such as the Numerical Rating Scale (NRS, 0-10 points) and the Facial Expression Pain Rating Scale (FPS-R). Patients can score themselves using a terminal or be assessed with the assistance of a nurse.

[0050] Pain feature extraction includes pain intensity, location, nature (e.g., stabbing, throbbing, pulling pain), duration, and triggering and relieving factors.

[0051] Pain correlation analysis: Time-series correlation analysis was conducted between pain scores and contemporaneous physiological parameters (such as heart rate variability, changes in respiratory pattern), activity levels, medication events, etc., to assess the degree of impact of pain on rehabilitation performance.

[0052] Pain trend modeling: Establish a patient's personal pain baseline model, identify whether the pain pattern is abnormal, such as a trend of worsening pain instead of relief, and evaluate the effect of analgesia regimen in combination with drug intervention records.

[0053] Example of muscle strength grading and training adjustment: Based on patients' muscle strength assessment results (such as MRC muscle strength classification) and rehabilitation stage, the platform categorizes patients into active and passive types and develops differentiated muscle strength training strategies accordingly. Passive patients (muscle strength ≤ grade 3 or early postoperative period, or those with poor physical strength): The training primarily employs passive or assisted active training methods. The platform generates the following instructions: Passive joint range of motion training: With the assistance of a nurse or rehabilitation therapist, perform full range of motion exercises for all joints of the limbs, twice a day, for 10-15 minutes each time; Assisted active training: Use equipment such as resistance bands and pulleys to complete movements with reduced weight or assistance, such as assisted straight leg raises and assisted upper limb dumbbell lifting (0.5-1kg). Neuromuscular electrical stimulation (NMES): Targeting key muscle groups such as the quadriceps femoris and deltoid, electrical stimulation is used to induce contraction when there is no active contraction ability, thereby maintaining muscle volume and nerve conduction.

[0054] Active type patients (muscle strength ≥ grade 4, with relatively good baseline): The training primarily focuses on active resistance training, gradually increasing the load and complexity. The platform generates the following instructions: Active resistance training: Use resistance bands and small dumbbells (1-2kg) to perform multi-joint compound movements, such as seated leg raises, standing elbow flexions, and calf raises; Progressive load adjustment: Based on the degree of muscle fatigue after training (as measured by electromyography signals from wearable devices or patient self-assessment of fatigue) and heart rate recovery, increase the load by 5-10% weekly; Functional integration training: Combine breathing exercises with trunk stability exercises, such as bridge exercises and plank (initially knee support), to enhance core muscle groups and breathing coordination.

[0055] The platform dynamically adjusts the intensity, frequency, and content of training based on patients' daily training feedback data (such as completion rate, movement standard, fatigue response, and muscle strength improvement trend), realizing a safe and progressive muscle strength rehabilitation path from passive to active and from low load to high load.

[0056] In one specific embodiment, the patient panoramic status data is a multidimensional feature vector, which includes at least: a temporal physiological feature subvector derived from features derived from waveform analysis and trend extraction of continuously monitored physiological parameters.

[0057] For continuous physiological signals (such as ECG and blood oxygen saturation) from wearable devices, features are extracted from the time domain, frequency domain, and nonlinear dynamics perspectives using a sliding window approach. Time-domain features include mean, variance, and waveform indices; frequency-domain features are obtained by calculating the energy of each sub-band after wavelet transform of the signal; nonlinear features include calculating the sample entropy of the signal. These features collectively constitute the time-series physiological feature sub-vector.

[0058] Analysis method: A multi-dimensional feature extraction framework of "time domain-frequency domain-nonlinear dynamics" is adopted.

[0059] Temporal characteristics: Statistical characteristics: Calculations are performed on the original signal within the sliding window.

[0060] Mean, variance, skewness, kurtosis.

[0061] Waveform characteristics: Heart rate variability triangular index: The ratio of the area to the height of a histogram of all RR intervals.

[0062] Signal amplitude curve statistics: Calculate the mean and coefficient of variation of the signal envelope (which can be obtained through Hilbert transform).

[0063] Frequency domain characteristics: The signal is decomposed into different frequency subbands (such as extremely low frequency, low frequency, and high frequency bands corresponding to physiological significance) by wavelet transform.

[0064] Calculate the energy or power of each subband as a characteristic.

[0065] Formula Example (Wavelet Energy): If the wavelet coefficients of the signal at scale j are d j (k), then its energy is:

[0066] Calculate the ratio of low-frequency power to high-frequency power; this ratio is often related to autonomic nervous system function.

[0067] Nonlinear dynamic characteristics: Sample entropy: Used to quantify the complexity and irregularity of a signal. The lower the value, the more regular or periodic the signal.

[0068] Brief description of the calculation steps: For length of The time series x(i) is defined with template vectors. .

[0069] Define X m (i) and X m The distance (i) is the maximum absolute value of the difference between the corresponding elements of the two.

[0070] For each i, count the number of j (j≠i) whose distance is less than the tolerance r (usually 0.2 times the signal standard deviation), denoted as B. i .

[0071] calculate .

[0072] Increase the dimension to m+1 and repeat the above steps to obtain B. m +1(r).

[0073] .

[0074] Ultimately, the temporal physiological feature subvector is a concatenation of all the calculated feature values, for example:

[0075] The event-related feature subvectors characterize the temporal correlation strength between patient-reported events, medication events, environmental mutation events, and changes in physiological parameters.

[0076] When a key event type is preset, physiological responses are extracted within a specific time window after the event occurs and compared with the baseline state before the event. The correlation strength is quantified by calculating the significance level of standardized differences or hypothesis testing, forming an event-related feature sub-vector.

[0077] A time window-based causal / association strength quantification method is adopted.

[0078] Step 1: Event Definition and Time Alignment Events such as "patient reports chest pain", "taking immunosuppressants", and "sudden increase in outdoor PM2.5 concentration" are labeled as discrete time points t. event .

[0079] Step 2: Physiological response window extraction For each event type E k Define one or more observation windows .

[0080] Extract physiological features (such as V) within the window from the patient's panoramic status data. temporal Specific indicators, or raw statistical values), denoted as .

[0081] Step 3: Correlation Strength Calculation Method A (Baseline Comparison): Before calculating events The same physiological characteristics are used as the baseline.

[0082] Calculate the standardized difference (such as Z-score difference) or rate of change between the response window features and the baseline features.

[0083] Formula example (standardized mean difference):

[0084] Method B (Statistical Test Method - Applicable to Frequent Events): Collect all E in the patient's history k Physiological responses after an event occurs, and physiological states during randomly selected non-event time periods.

[0085] Use hypothesis testing (such as t-test) to calculate the significance level of physiological indicators between the event group and the non-event group. .

[0086] Will As one measure of the strength of association, a larger value indicates a more significant association between the event and the physiological change.

[0087] Step 4: Characterization For each predefined event type E k The corresponding correlation strength value S k (or a set of values) as features.

[0088] Finally, the event-related feature sub-vector can be represented as:

[0089] The individual baseline offset feature subvector quantifies the deviation of each current indicator from the statistical distribution of the individual dynamic baseline model.

[0090] A personal dynamic baseline model for each physiological indicator is established based on the patient's historical data during the stable period. The standardized residuals of the current indicator values ​​relative to their baselines are calculated in real time, and the Mahalanobis distance of multiple indicators is calculated to characterize the degree to which the overall state deviates from the individual's normal state, forming a personal baseline offset feature sub-vector.

[0091] Analysis method: A dynamic baseline model and multivariate deviation measurement were used.

[0092] Step 1: Establishing a Personal Dynamic Baseline Model Historical data of patients in a clinically stable period (such as a stable postoperative recovery period) were used, employing a sliding window exponentially weighted moving average and standard deviation model.

[0093] Formula (for a certain physiological indicator) In time (baseline) Mean baseline:

[0094] Standard deviation baseline:

[0095] Where α is the forgetting factor, which controls the rate of baseline updates.

[0096] Step 2: Calculation of instantaneous offset for a single variable For the observation at the current moment Calculate its standardized residual (Z-score): ; The value of |z| directly reflects the degree to which the indicator deviates from the individual's normal range.

[0097] Step 3: Multivariate Joint Migration Calculation Considering the correlation between physiological parameters, the Mahalanobis distance is calculated.

[0098] formula: ; Where X now It is a vector composed of multiple current physiological indicators, μ vector Σ is the vector of individual baseline means corresponding to these indicators, and Σ is the covariance matrix of these indicators calculated during the stable period. M By comprehensively considering the correlation between various indicators, it can more sensitively detect subtle abnormalities in multiple physiological systems.

[0099] Step 4: Trend Deviation Characteristics Calculate the difference between the trend (linear regression slope) of a certain indicator in the short term (e.g., the last 6 hours) and its long-term (e.g., a week) baseline trend.

[0100] Finally, the individual baseline offset feature subvector can be represented as: .

[0101] The dynamic risk assessment and decision-making module is communicatively coupled with the multi-source data perception and fusion module. It is used to perform real-time risk analysis based on the patient's panoramic status data, using a preset personal dynamic baseline model and a first artificial intelligence model, and output early warning signals for complications including acute rejection and infection.

[0102] In one specific embodiment, the dynamic risk assessment and decision engine module further includes: a risk quantification unit, used to calculate a comprehensive risk score and main risk source labels based on the early warning signal.

[0103] Comprehensive risk score calculation: The weighted geometric mean method is used to integrate multiple indicators:

[0104] in: s i Normalized risk indicators include: Repulsion probability (normalized to 0-1) Infection probability (normalized to 0-1) Physiological parameter deviation = the proportion of the current indicator that deviates from the individual baseline w i Weights (rejection: 0.4, infection: 0.4, deviation: 0.2) Risk source labeling identified: Calculate the contribution of each risk source:

[0105]

[0106] Select the label with the highest contribution as the primary source of risk. If the difference between the two contribution values ​​is less than 0.1, it is marked as "multiple risks". The rehabilitation needs quantification unit is used to infer and calculate the urgency scores for different rehabilitation dimensions such as exercise, medication, nutrition, and psychology based on the rehabilitation needs.

[0107] The urgency score is calculated based on a multi-criteria decision analysis model: Step 1: Determine the evaluation criteria For each rehabilitation dimension m, consider the following criteria: Current state gap:

[0108] Risk mitigation potential: (The partial derivative of the risk score with respect to the intervention is predicted by the second AI model.) Historical compliance:

[0109] Time sensitivity

[0110] Step 2: Criterion Normalization ; ;

[0111] ;

[0112] .

[0113] Step 3: Calculate the urgency score

[0114] Weighting example: Motion dimension: w G =0.3,w P =0.4,w C =0.2,w T =0.1 The decision rule unit is used to receive the comprehensive risk score, risk source labels and urgency scores of each dimension, and access the clinical knowledge base to generate the rehabilitation intervention instructions according to the preset risk-need matching rules.

[0115] Rule representation: A production rule system is used, and each rule takes the following form:

[0116] Example of a specific rule base: Rule 1 (High-risk infection scenarios):

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[0125] Rule 2 (Medium-risk sports rehabilitation scenarios):

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[0134] Rule Priority and Conflict Resolution: Risk level priority: High-risk rules (overall risk score > 0.7) take precedence over medium- and low-risk rules. Timeliness takes precedence: emergency interventions (such as medication) take precedence over long-term improvement interventions (such as nutrition). Resource optimization: Avoid generating more than 3 main intervention commands simultaneously; prioritize the top 3 based on urgency. Instruction parameterization generation:

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[0141] In one specific embodiment, the rehabilitation intervention instructions include at least: dynamic exercise prescription adjustment instructions, instructions for enhanced medication management and follow-up examinations, and instructions for personalized educational content delivery.

[0142] Dynamic exercise prescription adjustment instructions: When the comprehensive risk score is below the threshold and the urgency score of the exercise dimension is high, the recommended intensity, duration and rest interval parameters for this exercise are output through a calculation model based on the patient's historical exercise performance and real-time physiological data.

[0143] Target exercise intensity calculation (expressed as a percentage of heart rate reserve): I base =f(rehabilitation stage) / / Base strength percentage retrieved from the preset rehabilitation stage table, for example, 50% at week 4 post-surgery;

[0144] / / Final strength percentage, limited to Within the range Among them: I base Indicates the percentage of base strength. This represents the standard score of blood oxygen saturation at the current moment. Its absolute value directly reflects the degree of deviation from safety. self The patient's self-assessment of fatigue level (0-10 scale) is indicated. β represents the attenuation coefficients for safety and fatigue (e.g., both set to 0.15), I final This represents the calculated final strength percentage.

[0145] Recommended exercise duration calculation: i base =f(rehabilitation phase) / / Baseline duration obtained from the query, e.g., 20 minutes / / Adjustment based on comprehensive risk score / / Final recommended duration Among them, D base D represents the baseline duration, γ1 represents the risk adjustment factor (e.g., 0.25), which controls the degree to which risk reduces the duration, and D final Indicates the final recommended duration.

[0146] Enhanced medication management and follow-up instructions: When the risk source label is "suspected infection" and the urgency score of the medication dimension is high, the generated instructions include: sending an enhanced reminder to the patient's terminal, linking the smart pillbox record, and generating suggested follow-up items for the doctor's confirmation.

[0147] Personalized educational content push instructions: Based on the current risk source tags and the rehabilitation dimension with the highest urgency score, match and push targeted educational materials from the knowledge base.

[0148] In one specific embodiment, the first artificial intelligence model is a hybrid model combining a recurrent neural network or a temporal convolutional network with an anomaly detection algorithm. It establishes the personal dynamic baseline model by continuously learning the temporal patterns of the patient's physiological parameters. The triggering of the early warning signal is determined based on the deviation of the current data stream from the personal dynamic baseline model and the matching degree of a specific pathological pattern.

[0149] Model architecture: A multi-task temporal convolutional network is employed, which includes: 3 temporal convolutional layers (kernel size = 5, number of channels = [64, 128, 256]) Global pooling layer Two parallel fully connected output layers (corresponding to rejection response and infection risk, respectively). Real-time analysis process: Step 1: Input Data Preparation The patient's overall condition data (feature dimension d) from the most recent 24 hours is organized into a matrix.

[0150] Standardization process:

[0151] Step 2: Model Inference Forward propagation calculates the probability of risk: #Temporal Convolution Feature Extraction # Pooling to fixed length #σ is the sigmoid function .

[0152] Step 3: Early Warning Signal Generation Set thresholds (rejection: 0.7, infection: 0.8), and generate an alert when the probability exceeds the threshold. If the rejection probability is >0.7: Output a "Suspected acute rejection reaction" warning. If the infection probability is >0.8: Output a "suspected infection" warning. Step 4: Calculation of Early Warning Confidence Uncertainty estimation using Monte Carlo dropout: conduct Each forward propagation (with random dropout at each step) ; ; If the variance is greater than 0.1, the warning level should be lowered.

[0153] Based on the patient's overall status data and historical rehabilitation response data, a second artificial intelligence model is used to predict the future effects of different rehabilitation interventions and generate personalized inferences about current rehabilitation needs.

[0154] The model operation is divided into two phases: Training phase: Utilizing historical data (i.e., historical rehabilitation response data, in the form of (state S) t Intervention A t The new state S generated after execution t+1 The health benefits R obtained t The model parameters are trained sequentially to learn how to evaluate the long-term value of intervention actions.

[0155] Inference phase: Based on real-time patient panoramic status data S now The model performs forward computation and directly outputs the future effect prediction (value score) and the urgency ranking of each possible intervention action (probability distribution).

[0156] The entire process is end-to-end. The model automatically learns to extract key features from the state and associate them with the optimal rehabilitation decision by maximizing long-term health benefits.

[0157] Model input: Enhanced state representation The model's state input not only includes the current patient status data but also incorporates historical interaction information to better predict the future.

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[0159] in, It is the feature vector of the current patient's overall status data. It is the encoding vector of the intervention actions taken in the previous period. It is the instant reward obtained in the previous period. This reflects the recent trend in the status.

[0160] Future effect prediction: achieved through value function The model's value network It is responsible for predicting future effects. It receives a state S. t and a candidate intervention action It outputs a scalar Q value, which is a quantitative prediction of the long-term cumulative health benefits that can be obtained after taking action a.

[0161] Calculation formula (based on Bellman optimal equation):

[0162] Where r represents the immediate reward, and γ is the discount factor, indicating the degree of importance placed on future returns. Network parameters Learning by minimizing the timing difference error:

[0163] These are the target network parameters used for stable training.

[0164] Current rehabilitation needs inference: achieved through a strategy function. The model's policy network It is responsible for generating requirement inferences. It receives state S. t Output a probability distribution over all K predefined actions.

[0165] Calculation formula (Softmax is used in the output layer of the policy network):

[0166] Where f θ (S t ) is the K-dimensional vector output by the final fully connected layer of the policy network. Training objective: The policy network parameters θ are updated using algorithms such as Proximal Policy Optimization (PPO) with the goal of maximizing the expected cumulative reward while limiting the magnitude of policy updates to ensure stability.

[0167]

[0168] in It is the dominance function, which measures the action a. t Advantages or disadvantages relative to the average level. V(S) t ) is the state value function.

[0169] In one specific embodiment, the second artificial intelligence model is a reinforcement learning model or a causal inference model. Its state space includes the patient's panoramic state data, and its action space includes different types of rehabilitation exercise intensities, drug adjustment suggestions, nutrition plans and educational content. The reward function is comprehensively constructed based on the degree of improvement of physiological indicators collected subsequently, the avoidance of complications and patient compliance.

[0170] The reward function aims to quantify the long-term benefits of an action, and it consists of a weighted average of three sub-rewards: physiological improvement, complication avoidance, and compliance.

[0171] definition: ; in For example, it is advisable ; Sub-reward 1: Reward for improvement in physiological indicators This reward encourages physiological indicators to revert to an individual's baseline (health status).

[0172] Among them, z i (t) Z is the standard score (Z-score) of the i-th physiological indicator before the action is performed. i (t+1) It is the standard score after the action is performed (e.g., 6 hours later). Measure the amount of reduction in the absolute value of the offset. The greater the reduction, the greater the reward. Ensure the direction is correct. If the offset direction is opposite (one positive and one negative), it means the indicator may have crossed the baseline and shifted to the other side. This product is negative and will penalize this "overcorrection".

[0173] Sub-reward 2: Complication risk avoidance reward The reward or penalty action that leads to an increase in risk score encourages risk reduction.

[0174]

[0175] in, and These are the comprehensive risk scores before and after the action is performed. The function compresses the difference to the range (-1, 1), ensuring a stable reward scalar. Sub-reward 3: Patient compliance reward This reward encourages the model to recommend actions that patients are more likely to perform.

[0176] Ⅱ is an indicator function that takes the value 1 when the condition is true and 0 otherwise.

[0177] Based on the early warning signals and the inferred rehabilitation needs, and combined with a clinical knowledge base, dynamically adapted rehabilitation intervention instructions are generated.

[0178] The dynamic adaptation process for generating rehabilitation intervention instructions is as follows: The system first integrates the early warning signals (such as "suspected infection") output by the first artificial intelligence model with the rehabilitation needs inference (probability distribution of actions sorted by priority) output by the second artificial intelligence model; the decision rule unit retrieves the corresponding intervention template from the clinical knowledge base according to the preset risk-need matching rules, and dynamically calculates and adjusts the template parameters in combination with the patient's real-time panoramic status data (such as personal baseline offset characteristics), thereby generating specific, executable, and personalized instructions (for example, when the risk of infection increases and the need for medication is urgent, the system automatically generates enhanced medication management instructions that include enhanced reminder frequency, smart medicine box linkage lock, and specific follow-up examination item recommendations), ultimately achieving precise and automatic adaptation between rehabilitation strategies and the patient's instantaneous state.

[0179] The personalized intervention execution and feedback module is communicatively coupled with the dynamic risk assessment and decision-making module. It is used to issue and execute the rehabilitation intervention instructions through the user terminal, and to collect execution compliance data and patient status change data after execution, forming a feedback data stream.

[0180] In one specific embodiment, the personalized intervention execution and feedback module includes: a compliance quantification unit and a multi-channel execution unit.

[0181] The compliance quantification unit is used to quantify and score the degree of execution of each rehabilitation intervention instruction through user terminal interaction confirmation, smart pillbox opening records, and correlation analysis of wearable device exercise data.

[0182] The quantification process follows a four-step process: instruction parsing -> evidence collection -> sub-score calculation -> weighted fusion.

[0183] Instruction parsing: The system receives rehabilitation intervention instructions from the decision module and parses out the instruction type (exercise, medication, etc.) and key execution parameters (such as target exercise intensity, medication time, educational material ID, etc.).

[0184] Evidence collection: Based on the instruction type, asynchronously collect direct and indirect evidence related to instruction execution from different data sources. Direct evidence: User terminal interaction confirmation (such as clicking the "Completed" button).

[0185] Device log evidence: opening records of the smart pillbox, and movement start / end events recorded by the wearable device.

[0186] Physiological relevance evidence: physiological data changes recorded by wearable devices during command execution (such as increased heart rate during exercise, expected trends of specific physiological parameters after medication).

[0187] Subscore calculation: For each relevant data source, a subscore is calculated that reflects the extent to which its supported instructions have been executed, ranging from [0,1].

[0188] Weighted fusion: Based on the instruction type and the reliability of the data source, weights are assigned to each sub-score to calculate the final quantitative compliance score.

[0189] A multi-channel execution unit is used to decompose the rehabilitation intervention instructions into at least two forms suitable for mobile application messages, smart home device control instructions, and wearable device vibration reminders for coordinated distribution.

[0190] The closed-loop learning optimization module is communicatively coupled to the dynamic risk assessment and decision-making module and the personalized intervention execution and feedback module, respectively, and is used to receive the feedback data stream and to perform parameter optimization and incremental training on the first artificial intelligence model and the second artificial intelligence model.

[0191] In one specific embodiment, the optimization of the model by the closed-loop learning optimization module includes: optimization of the first artificial intelligence model: comparing the finally diagnosed complication event with the model's historical warning records; if the model misses or falsely reports, then using the patient's panoramic status data within the time window before and after the event as positive / negative samples to incrementally train the model.

[0192] Optimization of the second artificial intelligence model: The changes in physiological indicators after the patient executes the intervention instructions, as well as the corrections made by medical staff to the instructions, are used together as reward signals or new causal pairs to update the decision-making strategy of the second artificial intelligence model.

[0193] Please see Figure 2 As shown, a method for a smart, dynamic, adaptive lung transplantation postoperative rehabilitation management platform is provided, including: Step S1: Collecting multimodal heterogeneous data of patients, performing spatiotemporal alignment and feature fusion, and generating panoramic status data of patients.

[0194] Step S2: Based on the patient's panoramic status data and personal dynamic baseline, perform real-time risk analysis using a time-series deep learning model to generate early warning signals for complications.

[0195] Step S3: Based on the patient's panoramic status data and historical rehabilitation response data, infer personalized rehabilitation needs through a prediction model, and generate dynamic rehabilitation intervention instructions by combining the clinical knowledge base.

[0196] Step S4: Send the dynamic rehabilitation intervention command to the patient's terminal, monitor the execution of the command, and obtain compliance and physiological feedback data.

[0197] Step S5: Optimize the temporal deep learning model and the prediction model using the compliance and physiological feedback data to achieve dynamic closed-loop adjustment of rehabilitation management strategies.

[0198] In another embodiment, the patient, Mr. Zhang, a 58-year-old male, underwent a double lung transplant for end-stage pulmonary fibrosis. Six hours post-surgery, he regained consciousness from anesthesia, his vital signs were initially stable, and he was transferred to the transplant intensive care unit. The clinical goal was to initiate ultra-early rehabilitation while ensuring safety, prevent complications (especially pulmonary edema), and achieve first ambulation within 24 hours.

[0199] 2. Platform Initialization and Data Awareness The multi-source data sensing and fusion module has started working: Patient-side: Wearable devices continuously collect heart rate (HR) and blood oxygen saturation. Respiratory rate (RR) and activity level data; patient self-rated fatigue level (NRS 0-10) was 2 points.

[0200] At the hospital: the electronic medical record system accesses surgical records and intraoperative fluid intake and output; the monitor continuously transmits invasive arterial blood pressure (ABP) and central venous pressure (CVP) data; the nurse enters the current RASS sedation score as 0 (awake and calm) and muscle strength assessment as grade 4 (able to resist resistance).

[0201] Environmental factors: The temperature and humidity sensor data in the ward is stable.

[0202] Module processing: Align the above spatiotemporal data to generate panoramic patient status data V. now , which includes: V temporal Currently, HR visits are at a rate of 88 times per minute. (Nasal cannula oxygen inhalation 3L / min). .

[0203] V baseline Calculate the current CVP relative to the patient's individual dynamic baseline (postoperative stable period preset target <8). The Z-score of ) is -0.8, which is within the ideal range.

[0204] V event Mark "6 hours post-surgery" as a critical event time point.

[0205] 3. Dynamic risk assessment and decision-making First Artificial Intelligence Model (Risk Warning Model) Analysis: Based on V now Model calculation: Risk of pulmonary edema: Low (10%), due to low CVP, good oxygenation, and failure to meet the PGD grade 1 criteria. .

[0206] Risk of acute rejection: extremely low (<5%).

[0207] Overall risk score: 0.15 (low risk). Key risk label: "Low perfusion risk" (due to low CVP).

[0208] Output: No high-level warning signal, but a prompt "Be alert that insufficient volume may affect rehabilitation tolerance" is generated.

[0209] The second artificial intelligence model (rehabilitation needs inference model) predicts: based on V now And historical rehabilitation data (similar patients), model prediction: Action value: "Assisting to sit up at the bedside (10 minutes)" has the highest long-term health benefit predictor (Q value).

[0210] Demand inference: The urgency score for the sports rehabilitation dimension was the highest (0.7), followed by fluid management (0.6).

[0211] Output: Recommended action is A1: Initiate Level 1 rehabilitation (sit up at the bedside).

[0212] Decision rule engine operation: Received: Low risk score, low perfusion indication, high exercise urgency, recommended movement A1.

[0213] Searching the clinical knowledge base: The knowledge base has encoded the rule of "Ultra-early rehabilitation pathway": "When RASS≥0, muscle strength≥3, and hemodynamic stability (MAP≥65mmHg, no high-dose vasoactive drugs), progressive sitting up can be started 6-24 hours after surgery." Matching and Generation: Rule matching successful. Combined with the "low perfusion alert," the decision engine dynamically adapts to standard rehabilitation instructions, adding fluid status monitoring and safety constraints.

[0214] Generate dynamically adapted rehabilitation intervention instruction packages: Instruction I1 (Dynamic Exercise Prescription): "Within 30 minutes, assist the patient to slowly raise the head of the bed to 60° and maintain a sitting position for 10 minutes. Target parameters: target heart rate increase not exceeding 20% ​​of baseline (i.e., ≤106 beats / min)." Maintain ≥93%. If dizziness, cold sweats, or other symptoms occur, [further action may be taken]. "If the injury is less than 90%, immediately lie flat." Instruction I2 (Enhanced Monitoring and Fluid Management): "Simultaneously monitor changes in CVP and blood pressure before and after sitting up. Adaptation rule: If CVP decreases by >2 cmH2O or blood pressure decreases by >10% after sitting up, it indicates relative volume insufficiency. Consider a slow infusion of 100-150 ml of colloid solution and reassess." Instruction I3 (Educational Push): "Send a graphic and text message to the patient and their family: 'Precautions for sitting up for the first time: Get up slowly, take deep breaths, and inform us immediately if you experience any discomfort.'" 4. Personalized intervention implementation and feedback The personalized intervention execution and feedback module sends instructions I1, I2, and I3 synchronously to the nurse station and the patient's bedside screen via the ward tablet (user terminal).

[0215] The nurse instructed the patient to sit up as directed by I1. After 8 minutes of sitting, the patient's vital signs were: HR 100 bpm. 94%, blood pressure slightly decreased.

[0216] Feedback data collection: Compliance quantification unit: When the nurse clicks "Execute Instruction", the system records I1 execution rate as 100%; the patient reads the educational materials.

[0217] Physiological data: Real-time data stream displayed by wearable devices during sitting up. It once dropped to 93%, but the heart rate trend is rising.

[0218] Nurse assessment: Observation results entered through the terminal: "Patient tolerated the condition reasonably well, with no complaints of discomfort, and was able to remain seated for 9 minutes." Lower the knee slightly, assisting the patient to lie flat one minute beforehand. After lying flat... It quickly recovered to 96%. Forming a feedback data stream: {Instruction set: I1, I2, I3; Execution degree: 1.0, 1.0, 0.8; Physiological response:} The lowest value was 93%, and the heart rate changed by +12 beats / min; clinical observation: early termination, volume status needs to be optimized.

[0219] 5. Closed-loop learning optimization The closed-loop learning optimization module receives feedback data streams.

[0220] Optimization of the first AI model: No pulmonary edema occurred in this incident, the model did not give false alarms, and no update is required.

[0221] Optimization of the second artificial intelligence model: Reward Calculation: According to the reward function, the immediate reward r obtained from this action A1 is moderate. Physiological improvement reward r phys Negative (because) (temporary decline), risk aversion reward r risk If positive (no adverse event occurred), the compliance reward r adh high.

[0222] Strategy Update: This serves as a storage of empirical samples. The model is updated through reinforcement learning algorithms, and in the future, when faced with similar states of "low CVP and high exercise demand," it may be more inclined to suggest a more aggressive capacity assessment or pre-supplementation before exercise, or to suggest a shorter initial sit-up time.

[0223] Tips for the clinical knowledge base: The platform can mark "This patient is more sensitive to changes in oxygenation caused by changes in body position" for medical staff to refer to.

[0224] Example of a Post-Lung Transplant Companionship Training and Guidance Program I. Basic Information of the Patient The patient, Ms. Li, 45 years old, underwent a single lung transplant for idiopathic pulmonary fibrosis and was transferred to a general ward on the 7th day after the operation.

[0225] Physiological status: heart rate 78 bpm, blood oxygen saturation 95% (2 L / min via nasal cannula), respiratory rate 18 breaths / min, muscle strength grade 3, no obvious pain (NRS score 1 point). The personal dynamic baseline model shows that the deviation of each indicator is within the safe range.

[0226] Basic rehabilitation: Bedside sitting training was completed on the 3rd day after surgery, compliance score was 82 (basically following instructions), and there were no warning signs of complications.

[0227] Characteristics of the needs: Unfamiliar with the rehabilitation training process and require detailed guidance; hospitalized alone and hoping to receive "real-time companionship" feedback to alleviate loneliness.

[0228] II. Analysis of Platform Data Collection and Training Requirements 1. Multi-source data perception and fusion On the patient side: Wearable devices collect heart rate, blood oxygen, respiratory rate and activity level in real time; patients report "mild shortness of breath" (subjective feeling score of 3 points) and "want to resume independent activities as soon as possible" through the APP.

[0229] On the hospital side: the electronic medical record shows that the postoperative recovery is stable, with no signs of infection or rejection; the nurse entered "weak airway clearance ability".

[0230] Environmental aspects: The temperature and humidity in the ward are suitable, and the air quality is excellent (PM2.5 < 35 μg / m³).

[0231] The platform integrates and generates panoramic status data: the temporal physiological characteristics are stable, the event correlation characteristics show that "shortness of breath is positively correlated with increased activity level", and the individual baseline offset characteristics are normal.

[0232] 2. Dynamic risk assessment and decision-making The first AI model has a comprehensive risk score of 0.2 (low risk), no complication warnings, and outputs a prompt that "insufficient airway clearance requires key intervention".

[0233] The second AI model predicts rehabilitation needs, with an urgency score of 0.8 (highest) for airway clearance training and 0.6 for exercise rehabilitation, generating a need inference of "prioritizing airway clearance + low-intensity breathing training".

[0234] Decision-making rule unit: Combined with clinical knowledge base, it generates "accompaniment-style training guidance plan", which includes three core functions: real-time guidance, interactive feedback and safety monitoring.

[0235] III. Phased Accompaniment Training Guidance Plan (10 days in total, twice a day, 30 minutes each time) Phase 1: Basic Adaptation Period (Days 7-8 Post-Surgery) – Introduction to Airway Clearance and Breathing Training Training objectives: Master the correct methods of coughing and expectorating phlegm and abdominal breathing, adapt to the training rhythm, and avoid worsening of shortness of breath.

[0236] Real-time support and guidance process (10:00-10:30 AM and 4:00-4:30 PM daily) Pre-training reminder (15 minutes before training) Instructions are issued simultaneously by execution units from multiple channels: The mobile app sent a text and voice message: "Ms. Li, your rehabilitation training will begin in 15 minutes. Please sit up straight with your back against the headboard and have tissues ready (for clearing phlegm). We will guide you step by step, don't worry." The wearable device vibrates to alert you, and the screen displays "Training countdown: 15 minutes".

[0237] Training guidance (real-time audio + video synchronization) Step 1: Airway clearance training (10 minutes) The app automatically plays animated tutorials with real-time voice guidance: "Now follow me to do the 'Panic Exhalation Method'—take a deep breath, hold it for 2 seconds, then exhale quickly as if exhaling warm air, making a 'ha' sound. Repeat 3 times as a set. Let's start the first set now~" After each set, the voice feedback is: "Great job! You did it perfectly~ Rest for 20 seconds, get ready for the second set, and pay attention to the movement of secretions in your airway~" If the wearable device detects a respiratory rate exceeding 22 breaths per minute, a voice prompt will immediately remind you: "Slow down your breathing. You're breathing a bit too fast right now. Take three deep breaths first, then continue. Your blood oxygen level is still stable, don't worry." Step 2: Abdominal breathing training (15 minutes) The app pushes a live demonstration video with synchronized voice guidance: "Place your hands on your abdomen. When you inhale, your stomach expands like blowing up a balloon. When you exhale, your stomach slowly contracts. Inhale through your nose and exhale through your mouth, maintaining the rhythm of 'inhale for 4 seconds, pause for 2 seconds, exhale for 6 seconds.' I'll count with you: 1-2-3-4 (inhale), pause (2 seconds), 1-2-3-4-5-6 (exhale)~" Every 5 minutes, a voice interaction: "Ms. Li, your belly is moving very noticeably now, your method is perfect! Keep going for another 5 minutes, and we'll complete today's core training~" Step 3: Relax and adjust (5 minutes) The voice prompt plays soft music: "Training is almost over. Let's relax together, breathe slowly, and feel the changes in your body. You did exceptionally well today; your shortness of breath didn't worsen, and your blood oxygen level remained above 95% throughout!" Real-time security monitoring The platform continuously synchronizes wearable device data. If blood oxygen saturation is <93% or heart rate is >100 beats / min, it will immediately trigger the following: The wearable device vibrates and gives a voice alert: "Attention! Your blood oxygen level is slightly low. We recommend pausing your training and resting in a semi-recumbent position. We have notified the nurses to monitor you." The app pops up a window displaying "rest suggestions" and automatically synchronizes data to the nurse's terminal.

[0238] Post-class feedback and encouragement Five minutes after the training ended, the app sent a personalized feedback message: "Ms. Li, you completed both training sessions today! Your compliance with airway clearance training was 100%, and your standard of abdominal breathing was 85 points (an improvement of 10 points from the first time). We will slightly increase the training time tomorrow. Keep up the good work!" Phase Two: Capacity Enhancement Period (Days 9-12 Post-Surgery) – Airway Clearance + Progressive Respiratory Muscle Training Training objectives: It strengthens respiratory muscles, improves airway clearance efficiency, and enables individuals to independently complete moderate-intensity breathing exercises, thus relieving shortness of breath symptoms.

[0239] Real-time support and guidance process (10:00-10:30 AM and 4:00-4:30 PM daily) Pre-training interaction confirmation The app popped up a questionnaire: "Ms. Li, how are you feeling today? Has your shortness of breath improved? (A. Significantly improved B. No change C. Worsened)" After the patient selected "A", the voice feedback was: "Great! Today we'll add respiratory muscle training, gradually increasing the difficulty. I'll stay with you the whole time, so please let me know if you experience any discomfort." Core training guidance (real-time parameter adjustment) Airway clearance training (12 minutes): Based on the "panting method for expectoration", add "back percussion assistance" guidance. Voice prompt: "Now please use a hollow fist to gently tap your back from bottom to top (a nurse can assist you). After tapping 3 times, do a panting to expectorate. Let's do it together: tap-tap-tap-panting!" The platform adjusts the pace in real time based on the activity level monitored by the wearable device: "Your movements are very stable right now, let's shorten the interval between each set to 15 seconds and speed things up a bit more!" Respiratory muscle training (15 minutes): Introduce "balloon blowing simulation training". The APP displays a virtual balloon and provides voice guidance: "Now imagine there is a balloon in front of you. Inhale and slowly blow out the air to make the virtual balloon bigger. Hold for 5 seconds and then exhale. See, you have blown the balloon to a medium size. Try harder and try to blow it to the biggest size!" If the patient's exhalation time reaches 5 seconds, the voice will praise them in real time: "Great job! Your respiratory muscle strength is improving. This set is perfect. Rest for 10 seconds and continue~" If the target is not met, the voice will say: "It's okay, let's try again. Inhale a little more and exhale a little slower. I'll count with you: 1-2-3-4-5~" Relaxation and stretching (3 minutes): Voice-guided shoulder and neck stretches: "Cross your hands in front of your chest, slowly rotate your body to the left and right, relax your breathing muscles, hold for 5 seconds on each side, follow me: Left-Pause-Right-Pause~" Interactive feedback and Q&A During training, patients can click the "Question" button on the app and receive a real-time voice reply: "Ms. Li, do you feel weak when exhaling? That's okay, we'll adjust the goal to 'hold for 3 seconds,' adapt first and then improve. You're already doing great!" After the training, a data report was sent out: "Today's respiratory muscle training average exhalation time was 4.2 seconds (0.8 seconds better than yesterday), airway clearance and sputum expectoration increased compared to before, and the shortness of breath score dropped from 3 to 2. Keep it up!" Phase Three: Consolidation Period (Days 13-16 Post-Surgery) – Comprehensive Training + Self-Management Guidance Training objectives: Independently complete the entire training program and master the method of adjusting the intensity according to the physical condition, laying the foundation for rehabilitation after discharge from the hospital.

[0240] Real-time support and guidance process (10:00-10:30 AM daily) Self-planning and guidance The app pushes a selection option: "Ms. Li, would you like to do airway clearance or respiratory muscle training first today? Would you like the training duration to remain at 30 minutes or be extended to 35 minutes?" The patient selected "Airway clearance priority + 30 minutes," and the voice feedback was: "Okay, the training plan will be generated according to your selection. If you want to make adjustments during the process, you can click the 'Pause / Adjust' button on the app at any time." Semi-autonomous training + real-time supervision The patient completes the training steps independently, with the platform providing intermittent voice reminders: "This is the 5th minute of airway clearance training. Your breathing is very steady, keep it up~" "There are 10 minutes left in the training, do you want to speed up the pace a bit?" If the wearable device detects insufficient activity from the patient (failure to complete the planned actions), it will ask in voice: "Ms. Li, are you a little tired? If you are, you can rest for 2 minutes before continuing. We're not in a hurry; safety comes first." Discharge preparation guidance After the final training session, the app pushed out a "Self-Training Guide" video with a voice summary: "Ms. Li, your 10-day training program is over! You are now able to independently complete airway clearance and respiratory muscle training, and your shortness of breath symptoms have significantly improved. After discharge, continue this pace, training twice a day. The app will continue to send reminders and monitor your progress. If you have any questions, please contact us through the app. We wish you a speedy recovery!" IV. Closed-loop optimization and adjustment (throughout the entire process) Daily training data feedback Automatic scoring of compliance quantification unit: If the patient completes all training, the score is 100 points, and verbal praise is given; if there is a pause, the score is 85 points, and the message "Try to complete it completely tomorrow, you can definitely do it!" is displayed. Physiological data feedback: If blood oxygen levels improve and shortness of breath scores decrease after training, the second AI model will increase the recommendation weight for similar training; if shortness of breath worsens, the training duration for the next day will be automatically shortened by 5 minutes.

[0241] Remote intervention by medical staff Nurses view training data through a B-end terminal. If a patient's airway clearance training accuracy score is below 70 points for two consecutive sessions, a video call is initiated via the app: "Ms. Li, I see your coughing and expectoration movements are a bit off. I'll demonstrate again now, and you can follow along." Implementation effect

[0242] From the patient's perspective: After 10 days of training, the airway clearance ability was significantly improved, the shortness of breath score dropped from 3 to 1, and the patient could complete 30 minutes of training independently without any complications. The patient reported that "the APP is like a personal rehabilitation coach, providing real-time guidance and encouragement, so I am not alone at all."

[0243] From a medical perspective: No need for full-time on-site accompaniment; only 1-2 remote guidance sessions are required for patients with low compliance and low standards, saving approximately 60 minutes of human accompaniment time per patient per day.

[0244] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A smart, dynamically adaptable post-lung transplant rehabilitation management platform, characterized in that, include: The multi-source data perception and fusion module is used to collect multimodal heterogeneous data from the patient, hospital and environment, and perform spatiotemporal alignment and feature fusion processing on the multimodal heterogeneous data to generate panoramic patient status data. The dynamic risk assessment and decision-making module is communicatively coupled with the multi-source data perception and fusion module. It is used to perform real-time risk analysis based on the patient's panoramic status data, using a preset personal dynamic baseline model and a first artificial intelligence model, and output early warning signals for complications including acute rejection and infection. Based on the patient's overall status data and historical rehabilitation response data, a second artificial intelligence model is used to predict the future effects of different rehabilitation interventions and generate personalized inferences about current rehabilitation needs. Based on the early warning signals and the inference of rehabilitation needs, and combined with the clinical knowledge base, dynamically adapted rehabilitation intervention instructions are generated. The personalized intervention execution and feedback module is communicatively coupled with the dynamic risk assessment and decision-making module. It is used to issue the rehabilitation intervention instructions through the user terminal and collect execution compliance data and post-execution patient status change data to form a feedback data stream. The closed-loop learning optimization module is communicatively coupled to the dynamic risk assessment and decision-making module and the personalized intervention execution and feedback module, respectively, and is used to receive the feedback data stream and to perform parameter optimization and incremental training on the first artificial intelligence model and the second artificial intelligence model.

2. The intelligent dynamic adaptive lung transplant postoperative rehabilitation management platform according to claim 1, characterized in that: The patient panoramic status data is a multidimensional feature vector, which includes at least: The time-series physiological feature subvectors are derived from the waveform analysis and trend extraction of continuously monitored physiological parameters; Event-related feature subvectors characterize the temporal correlation strength between patient-reported events, medication events, environmental mutation events, and changes in physiological parameters; The individual baseline offset feature subvector quantifies the deviation of each current indicator from the statistical distribution of the individual dynamic baseline model.

3. The intelligent dynamic adaptive lung transplant postoperative rehabilitation management platform according to claim 1, characterized in that: The dynamic risk assessment and decision-making engine module further includes: The risk quantification unit is used to calculate a comprehensive risk score and main risk source labels based on the early warning signals. A rehabilitation needs quantification unit is used to infer and calculate urgency scores for different rehabilitation dimensions based on the rehabilitation needs. The decision rule unit is used to receive the comprehensive risk score, risk source labels and urgency scores of each dimension, and access the clinical knowledge base to generate the rehabilitation intervention instructions according to the preset risk-need matching rules.

4. The intelligent dynamic adaptive lung transplant postoperative rehabilitation management platform according to claim 1, characterized in that: The rehabilitation intervention instructions include at least: instructions for adjusting dynamic exercise prescriptions, instructions for strengthening medication management and follow-up examinations, and instructions for pushing personalized educational content.

5. The intelligent dynamic adaptive lung transplant postoperative rehabilitation management platform according to claim 1, characterized in that: The first artificial intelligence model is a hybrid model combining a recurrent neural network or a temporal convolutional network with an anomaly detection algorithm. It establishes the personal dynamic baseline model by continuously learning the temporal patterns of the patient's physiological parameters. The triggering of the early warning signal is determined based on the deviation of the current data stream from the personal dynamic baseline model and the matching degree of a specific pathological pattern.

6. The intelligent dynamic adaptive lung transplant postoperative rehabilitation management platform according to claim 1, characterized in that: The second artificial intelligence model is a reinforcement learning model or a causal inference model. Its state space includes the patient's panoramic state data, and its action space includes different types of rehabilitation exercise intensities, drug adjustment suggestions, nutrition plans and educational content. The reward function is comprehensively constructed based on the degree of improvement of physiological indicators collected subsequently, the avoidance of complications and patient compliance.

7. The intelligent dynamic adaptive lung transplant postoperative rehabilitation management platform according to claim 1, characterized in that: The personalized intervention execution and feedback module includes: a compliance quantification unit and a multi-channel execution unit; The compliance quantification unit is used to quantify and score the degree of execution of each rehabilitation intervention instruction through user terminal interaction confirmation, smart pillbox opening records, and correlation analysis of wearable device exercise data. A multi-channel execution unit is used to coordinate the issuance of the rehabilitation intervention instructions.

8. The intelligent dynamic adaptive lung transplant postoperative rehabilitation management platform according to claim 1, characterized in that: The closed-loop learning optimization module optimizes the model in the following ways: Optimization of the first artificial intelligence model: The finally diagnosed complication events are compared with the model's historical warning records. If the model misses or falsely reports an event, the patient's overall status data within the time window before and after the event is used as positive / negative samples to incrementally train the model. Optimization of the second artificial intelligence model: The changes in physiological indicators after the patient executes the intervention instructions, as well as the corrections made by medical staff to the instructions, are used together as reward signals or new causal pairs to update the decision-making strategy of the second artificial intelligence model.

9. A method for implementing the intelligent dynamic adaptive lung transplant postoperative rehabilitation management platform according to any one of claims 1-8, characterized in that: include: Step S1: Collect multimodal heterogeneous data of patients, and perform spatiotemporal alignment and feature fusion to generate panoramic status data of patients; Step S2: Based on the patient's panoramic status data and personal dynamic baseline, perform real-time risk analysis using a time-series deep learning model to generate early warning signals for complications; Step S3: Based on the patient's panoramic status data and historical rehabilitation response data, infer personalized rehabilitation needs through a prediction model, and generate dynamic rehabilitation intervention instructions by combining the clinical knowledge base; Step S4: Send the dynamic rehabilitation intervention command to the patient's terminal, monitor the execution of the command, and obtain compliance and physiological feedback data; Step S5: Optimize the temporal deep learning model and the prediction model using the compliance and physiological feedback data to achieve dynamic closed-loop adjustment of rehabilitation management strategies.

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