Motion intervention multi-dimensional decision support system and method for CIPN alleviation

Abstract

The invention discloses a motion intervention multi-dimensional decision support system and method for CIPN remission, and relates to the technical field of decision support, and the system comprises an intervention effect prediction unit which is used for collecting the multi-dimensional data of a patient, constructing a patient simulation model, and carrying out the simulation of different motion intervention schemes through the Markov decision process theory, establishing an intervention effect prediction model based on the simulation result; an exercise intervention recommendation unit; an encouraging and exciting unit; the optimal motion recommendation unit is used for updating a patient simulation model according to the constructed encouraging and incentive mechanism, analyzing a causal relationship between a motion effect and neural restoration through a hypergraph gradient causal discovery algorithm, and screening out an optimal motion recommendation scheme; and a visualization unit. According to the invention, a personalized exercise intervention scheme can be customized according to the individual characteristics and physiological status of the patient, and the effects of different intervention strategies are predicted, so that the pertinence and effectiveness of exercise treatment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of decision support, in particular to a multi-dimensional decision support system and method for exercise intervention for CIPN relief. BACKGROUND

[0002] Breast cancer has become one of the highest incidence of malignant tumors in women worldwide, accounting for about 30% of female cancers. Chemotherapy, as a key component of comprehensive treatment of breast cancer, plays an important role in prolonging the survival of patients, reducing the risk of recurrence and improving long-term prognosis. However, the use of chemotherapeutic drugs is often accompanied by a series of adverse reactions, among which chemotherapy-induced peripheral neuropathy (CIPN) is particularly common. CIPN is a complex neuropathic state caused by neurotoxic chemotherapy drugs (such as paclitaxel, cisplatin, vinca alkaloids, etc.) damaging the peripheral nervous system, and its clinical manifestations mainly include numbness, tingling, burning sensation, motor disorders, and reflex reduction in the limbs. According to relevant research reports, the incidence of CIPN can reach 11% to 80%, and some patients still have CIPN-related symptoms 1 to 3 years after the end of chemotherapy. This symptom not only seriously affects the daily life ability and physical activity level of breast cancer patients, but also may lead to the intolerance of patients to chemotherapy, thereby interrupting or abandoning treatment, affecting the overall treatment effect and survival rate.

[0003] Current treatment for CIPN still mainly relies on symptomatic relief of drugs, and there is still a lack of clear and effective prevention or reversal strategies. In recent years, non-pharmacological intervention methods have gradually attracted attention, especially exercise intervention, which is considered a potential auxiliary intervention method due to its high safety, good compliance, and no drug toxicity. A number of review studies have shown that scientific and reasonable exercise intervention can reduce the incidence and severity of CIPN through mechanisms such as improving neural regulation, enhancing muscle strength, and promoting nerve regeneration, and can help improve the physical fitness and quality of life of patients.

[0004] However, current research on exercise intervention for CIPN relief mostly adopts a single and standardized intervention program, without fully considering individual differences among patients. Existing exercise prescriptions often fail to combine the actual condition, functional status, and risk assessment of patients, making it difficult to achieve personalized and precise treatment. In addition, the effectiveness of exercise intervention is also affected by patient compliance, exercise type, intensity, and frequency, and the combined effects of these factors make the intervention effect less than ideal.

[0005] In view of the problems in the related art, no effective solutions have been proposed so far. SUMMARY

[0006] In view of the problems in the related art, the application provides a multi-dimensional decision support system and method for exercise intervention for CIPN relief to overcome the above technical problems existing in the prior art.

[0007] To this end, the application adopts the following specific technical solutions:

[0008] According to one aspect of the application, a multi-dimensional decision support system for exercise intervention for CIPN relief is provided, which comprises:

[0009] An intervention effect prediction unit is configured to collect multi-dimensional data of a patient, build a patient simulation model, and simulate different exercise intervention schemes using Markov decision process theory, and establish an intervention effect prediction model based on the simulation results;

[0010] An exercise intervention recommendation unit is configured to use federated learning technology to aggregate and learn multi-dimensional data of different medical centers based on the intervention effect prediction model, and combine patient community crowdsourcing case data and medical knowledge to build an exercise intervention recommendation model;

[0011] An encouragement and incentive unit is configured to convert the exercise recommendation scheme output by the exercise recommendation model into a game-like virtual task scene through mixed reality technology, and identify the emotional state of the patient using a hidden Markov model, and establish an encouragement and incentive mechanism based on the identification results;

[0012] An optimal exercise recommendation unit is configured to update the patient simulation model based on the established encouragement and incentive mechanism, and analyze the causal relationship between exercise effect and nerve repair through a hypergraph gradient causal discovery algorithm to screen out an optimal exercise recommendation scheme;

[0013] A visualization unit is configured to convert the selected optimal exercise recommendation scheme into a multi-dimensional visualization chart to support joint decision-making by the patient and the doctor.

[0014] Optionally, the intervention effect prediction unit comprises:

[0015] A data acquisition and processing module is configured to collect multi-dimensional data of a patient, and build a standardized feature dataset through data cleaning and feature extraction;

[0016] A patient simulation model construction module is configured to generate corresponding mathematical expressions based on the constructed feature dataset, and build a patient simulation model in combination with a heterogeneous cellular automaton;

[0017] A simulation dataset generation module is configured to build a simulation environment using Markov decision process theory based on the constructed patient simulation model, and generate a simulation dataset under different exercise intervention schemes in combination with a deep reinforcement learning algorithm;

[0018] The prediction model establishment module is configured to combine the simulation dataset and the feature dataset, construct a time-series feature matrix, and use a deep neural network architecture to model the spatiotemporal dependency relationship between the exercise intervention scheme and the health index change, thereby constructing an intervention effect prediction model.

[0019] Optionally, the patient simulation model construction module comprises:

[0020] The mathematical expression generation submodule is configured to use symbolic regression and sparse dynamic identification technology to analyze the implicit dynamics equation of the physiological system of the patient based on the constructed feature dataset, and generate a corresponding mathematical expression.

[0021] The digital twin simulation engine construction submodule is configured to combine the mathematical expression and the heterogeneous cellular automaton to construct a digital twin simulation engine for cross-scale interactive simulation.

[0022] The simulation model construction submodule is configured to establish a geometric topology graph, use an isometric graph neural network to perform isometric coding learning on the geometric topology graph, and combine the digital twin simulation engine to construct a patient simulation model.

[0023] Optionally, the establishment of the geometric topology graph, the use of the isometric graph neural network to perform isometric coding learning on the geometric topology graph, and the combination of the digital twin simulation engine to construct the patient simulation model comprises:

[0024] Based on the feature dataset, the body structure features of the patient are extracted, and a geometric topology graph representing the spatial position and functional correlation relationship between body tissues is established.

[0025] The physical interaction behavior of each node and edge in the geometric topology graph is simulated based on the rigid body dynamics principle and the graph Laplacian operator, and a graph structure dynamics equation of the patient body structure evolution is constructed.

[0026] According to the graph structure dynamics equation, an isometric graph neural network model embedded with rigid motion constraints is designed to perform isometric coding learning on the geometric topology graph, thereby capturing the individualized physiological motion pattern of the patient and the nonlinear structural changes in the training process.

[0027] The learning result of the isometric graph neural network is coupled with the digital twin simulation engine to construct the patient simulation model.

[0028] Optionally, the simulation dataset generation module comprises:

[0029] The simulation environment construction submodule is configured to construct a simulation environment of the exercise intervention scheme based on the patient simulation model and the Markov decision process theory, and define a multi-dimensional state space, a discrete action space, a state transition probability, a multi-dimensional reward function, and a discount factor.

[0030] The intervention scheme pre-training submodule is configured to pre-train a preset exercise intervention scheme by using a deep reinforcement learning algorithm based on the constructed exercise intervention scheme simulation environment.

[0031] The iteration and simulation data generation submodule is configured to iteratively optimize the pre-training result by behavior comparison and scheme migration according to an imitation learning mechanism, and generate simulation data sets under different exercise intervention schemes.

[0032] Optionally, the encouragement and incentive unit comprises:

[0033] The virtual task scene generation module is configured to map the exercise parameters in the exercise recommendation scheme to the mixed reality environment by using a dynamic rule engine, and generate a game-like virtual task scene in combination with a generative adversarial network.

[0034] The emotion feature extraction module is configured to collect physiological data and behavior data of the patient based on the generated virtual task scene, and extract emotion features by using a multi-modal fusion network.

[0035] The task adjustment scheme generation module is configured to identify the emotional state of the patient by using a hidden Markov model based on the extracted emotion features, and generate a personalized task adjustment scheme in combination with an inverse reinforcement learning to infer the patient's preference for task difficulty under different emotional states.

[0036] The mechanism establishment module is configured to analyze the emotional state of the patient by using emotional computing technology based on the personalized task adjustment scheme, and establish a personalized encouragement and incentive mechanism in combination with a pre-trained language generation model.

[0037] Optionally, the mechanism establishment module comprises:

[0038] The emotion label knowledge graph construction submodule is configured to construct an emotion label knowledge graph by taking emotion categories as nodes and semantic associations between emotions as edges based on the identified emotional state of the patient.

[0039] The emotion representation vector construction submodule is configured to analyze the emotion label knowledge graph by using an attention mechanism, extract hierarchical semantic features between emotions, and construct an emotion representation vector.

[0040] The control variable generation submodule is configured to jointly encode the personalized task adjustment scheme and the emotion representation vector by using a pre-set variational control autoencoder, and generate a control variable with emotional semantic information.

[0041] The emotional semantic information injection submodule is configured to input the control variable into the pre-trained language generation model, and normalize the injection of emotional semantic information by using a conditional layer.

[0042] The text output and mechanism establishment submodule is configured to design a hybrid reward function including task completion degree, emotional change trend and user response based on the injection result, guide the language generation model to output multi-modal feedback text including personalized encouragement rhetoric, emotional support sentence and task adjustment suggestion, and establish an encouragement and motivation mechanism.

[0043] Optionally, the optimal exercise recommendation unit comprises:

[0044] The patient simulation model updating module is configured to collect feedback data of the patient after the encouragement and motivation mechanism is executed, and update the patient simulation model by variational Bayesian inference using the feedback data as observation evidence.

[0045] The causal path identification and network construction module is configured to identify causal paths between exercise intervention variables and neural repair indicators using a hypergraph gradient causal discovery algorithm based on the updated patient simulation model, and construct a causal reasoning network by quantifying the confidence interval of each causal path using Markov Chain Monte Carlo sampling.

[0046] The candidate exercise recommendation scheme set establishment module is configured to search for exercise recommendation schemes that satisfy causal logic using a quantum annealing algorithm based on the preset multi-objective optimization framework and the causal reasoning network as constraint conditions, and establish a candidate exercise recommendation scheme set.

[0047] The optimal exercise recommendation scheme screening module is configured to perform causal stability evaluation and screening on each exercise recommendation scheme in the candidate exercise recommendation scheme set using a counterfactual robustness verification mechanism, and obtain an optimal exercise recommendation scheme based on the screening result.

[0048] Optionally, the causal path identification and network construction module comprises:

[0049] The correlation mining submodule is configured to extract feature information of exercise intervention variables and neural repair-related indicators based on the updated patient simulation model, construct initial feature representations of hypergraph nodes, and mine correlations between variables using a hypergraph convolution network for multi-order neighborhood information transmission.

[0050] The causal path screening submodule is configured to identify causal paths between exercise intervention variables and neural repair indicators using a causal structure learning algorithm based on the correlation mining result, and screen the causal paths using a Monte Carlo tree search algorithm.

[0051] The confidence interval calculation submodule is configured to model the weight of the screened causal paths as a Gaussian random variable, estimate the posterior distribution by Markov Chain Monte Carlo sampling, and calculate the confidence interval of each causal path.

[0052] The cause-effect reasoning network construction submodule is configured to construct a cause-effect reasoning network by combining the identified cause-effect paths and the calculated confidence intervals.

[0053] According to another aspect of the present application, there is also provided a multi-dimensional decision support method for motion intervention for CIPN relief, the method comprising:

[0054] S 1, collecting multi-dimensional data of a patient, constructing a patient simulation model, and using Markov decision process theory to simulate different motion intervention schemes, and establishing an intervention effect prediction model based on the simulation results;

[0055] S2, according to the intervention effect prediction model, using federated learning technology to aggregate and learn multi-dimensional data of different medical centers, and combining patient community crowdsourcing case data and medical knowledge to construct a motion intervention recommendation model;

[0056] S3, converting the motion recommendation scheme output by the motion recommendation model into a gamified virtual task scene through mixed reality technology, and identifying the emotional state of the patient based on the hidden Markov model, and establishing an encouragement mechanism based on the identification results;

[0057] S4, updating the patient simulation model according to the constructed encouragement and incentive mechanism, and analyzing the causal relationship between motion effect and nerve repair through hypergraph gradient causal discovery algorithm, and screening the optimal motion recommendation scheme;

[0058] S5, converting the selected optimal motion recommendation scheme into a multi-dimensional visual chart to support joint decision-making by the patient and the doctor.

[0059] The beneficial effects of the present application are:

[0060] 1, the simulation model constructed based on the multi-dimensional data of the patient, and combined with the Markov decision process for simulation, can customize individualized motion intervention schemes according to the individual characteristics and physiological state of the patient, and predict the effects of different intervention strategies, thereby improving the pertinence and effectiveness of motion therapy, and enhancing the accuracy and safety of treatment.

[0061] 2, the present application converts the motion recommendation scheme into a gamified virtual task scene, and identifies the emotional state of the patient based on the hidden Markov model, and constructs a personalized encouragement and incentive mechanism, thereby improving the emotional support and behavior guidance effect, enhancing the participation and treatment initiative of the patient, and further improving the intervention compliance and overall efficacy.

[0062] 3、The application identifies the potential causal path between the motor intervention and the nerve repair by adopting the supergraph gradient causal discovery algorithm, and combines the Bayesian inference to model the uncertainty of the causal effect of each path and estimate the confidence interval, so as to screen out an individualized optimal motor intervention scheme; in addition, based on the construction of the causal reasoning network, a structured logical basis and interpretability are provided for the selection and adjustment of the intervention strategy, and the maximization of the intervention effect is ensured under the influence of complex variables. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0064] Figure 1 is a principle block diagram of a motor intervention multi-dimensional decision support system for CIPN relief according to an embodiment of the present application;

[0065] Figure 2 is a flowchart of a motor intervention multi-dimensional decision support method for CIPN relief according to an embodiment of the present application.

[0066] In the drawings:

[0067] 1, intervention effect prediction unit; 2, motor intervention recommendation unit; 3, encouragement and incentive unit; 4, optimal motor recommendation unit; 5, visualization unit. DETAILED DESCRIPTION

[0068] To further illustrate the embodiments, the present application provides drawings, which are part of the disclosure of the present application, mainly used to illustrate the embodiments, and can explain the operating principle of the embodiments in conjunction with the related description of the specification. With reference to these contents, those skilled in the art should understand other possible embodiments and advantages of the present application.

[0069] According to an embodiment of the present application, a motor intervention multi-dimensional decision support system and method for CIPN relief are provided.

[0070] The present application will be further described in conjunction with the drawings and specific embodiments, as shown in Figure 1 The motor intervention multi-dimensional decision support system for CIPN relief according to the embodiment of the present application comprises:

[0071] The intervention effect prediction unit 1 is used for collecting multi-dimensional data of a patient, constructing a patient simulation model, and simulating different exercise intervention schemes by using a Markov decision process theory, and establishing an intervention effect prediction model based on a simulation result.

[0072] In this optional embodiment, the intervention effect prediction unit 1 comprises:

[0073] A data collection and processing module is used for collecting multi-dimensional data of a patient, and constructing a standardized feature data set through data cleaning and feature extraction.

[0074] A patient simulation model construction module is used for generating a corresponding mathematical expression based on the constructed feature data set, and constructing a patient simulation model in combination with a heterogeneous cellular automaton.

[0075] In this optional embodiment, the patient simulation model construction module comprises:

[0076] A mathematical expression generation submodule is used for analyzing an implicit dynamic equation of a physiological system of a patient by using a symbolic regression and sparse dynamic identification technology based on the constructed feature data set, and generating a corresponding mathematical expression.

[0077] A digital twin simulation engine construction submodule is used for constructing a digital twin simulation engine in combination with the mathematical expression and the heterogeneous cellular automaton, so as to perform cross-scale interactive simulation.

[0078] A simulation model construction submodule is used for establishing a geometric topology graph, performing isometric coding learning on the geometric topology graph by using an isometric graph neural network, and constructing a patient simulation model in combination with the digital twin simulation engine.

[0079] It needs to be pointed out that the mathematical expression generation submodule searches for the combination of basis functions through the symbolic regression algorithm (such as the sparse regularization objective function of the PySR library) on the patient physiological feature dataset, dynamically identifies the key parameter dependence by using the deep symbolic regression framework driven by reinforcement learning (referring to the generative Transformer architecture of SymbolicGPT), and finally parses the implicit differential equation containing the correlation between heart rate variability and metabolic rate. Its advantage lies in obtaining sparse and interpretable expressions through L1 regularization constraints (such as restoring the nonlinear relationship equation between exercise intervention effect and inflammatory factor IL-6, with an error of less than 5%); the digital twin simulation engine construction submodule adopts the multi-physics simulation platform of GT-SUITE to dynamically link the mathematical expressions output by the symbolic regression with the heterogeneous cellular automaton model (processing cell-scale metabolism and organ-scale blood flow coupling), and realizes real-time parameter calibration driven by cross-center physiological data through the federated learning framework. This architecture successfully simulates the cross-scale interaction between neural repair and blood perfusion in the stroke rehabilitation scenario (with a prediction accuracy improvement of 27%); the simulation model construction submodule performs rotation and translation invariant vector coding on the patient organ geometric topology graph based on the E(n) equivariant graph neural network (EGNN block), combines the real-time biomechanical data stream of the digital twin engine, and constructs a patient simulation model with dynamic joint torque prediction capability (in the knee rehabilitation test, equivariant coding reduces the motion trajectory prediction error to 0.15DTW value), and enhances the generalization ability for extreme cases such as osteoporosis through adversarial training.

[0080] In this optional embodiment, the geometric topology graph is established, the equivariant graph neural network is used for equivariant coding learning of the geometric topology graph, and the patient simulation model is constructed in combination with the digital twin simulation engine, including:

[0081] Based on the feature dataset, the body structure features of the patient are extracted, and a geometric topology graph representing the spatial position and functional correlation between body tissues is established;

[0082] In combination with the rigid body dynamics principle and the graph Laplacian operator, the physical interaction behavior of each node and edge in the geometric topology graph is simulated, and a graph structure dynamics equation of the patient body structure evolution with motion is constructed;

[0083] According to the graph structure dynamics equation, an equivariant graph neural network model embedded with rigid motion constraints is designed for equivariant coding learning of the geometric topology graph, to capture the individualized physiological motion pattern of the patient and the nonlinear structural changes in the training process;

[0084] The learning results of the equivariant graph neural network are coupled with the digital twin simulation engine to construct a patient simulation model.

[0085] It needs to be pointed out that the geometric topology graph is established, the geometric topology graph is isometrically encoded and learned by using the isometric graph neural network, and the specific implementation of the patient simulation model is constructed by combining the digital twin simulation engine, for example as follows:

[0086] Based on the patient MRI / CT image feature dataset, the geometric topology graph is constructed by using 3D Slicer to extract the bone-muscle spatial topology relationship (the node is the joint / organ, and the edge is the mechanical transmission path); the graph structure dynamics equation under rigid body constraint is derived by combining Newton-Euler equation and graph Laplacian operator, for example, the knee joint torque balance equation:

[0087]

[0088] In the formula, M represents the inertia matrix related to the joint angle, represents the second order time derivative of the joint angle, C represents the Coriolis and centrifugal force term, represents the first order time derivative of the joint angle, F ext represents the external torque; the motion evolution is simulated by ODE solver; the SE(3)-isometric graph neural network is designed to embed the rigid body motion constraint (such as joint rotation invariance), and the model is trained by using the adversarial training to enhance the generalization ability of the model to gait abnormalities (such as hemiplegic patients); the NVIDIA PhysX engine is coupled to realize real-time simulation of digital twin, and the motion trajectory prediction error is reduced to 0.12 RMSE in the rehabilitation training after knee replacement, the real-time calculation frame rate is improved to 60 FPS, and the generalization error is reduced by 42% across patient groups.

[0089] The simulation dataset generation module is used to construct a simulation environment by using Markov decision process theory based on the constructed patient simulation model, and generate a simulation dataset under different motion intervention schemes by combining a deep reinforcement learning algorithm.

[0090] In this optional embodiment, the simulation dataset generation module comprises:

[0091] The simulation environment construction submodule is used to construct a simulation environment of the motion intervention scheme based on the patient simulation model and Markov decision process theory, and define a multi-dimensional state space, a discrete action space, a state transition probability, a multi-dimensional reward function and a discount factor;

[0092] The intervention scheme pre-training submodule is used to pre-train a preset motion intervention scheme by using a deep reinforcement learning algorithm according to the constructed motion intervention scheme simulation environment;

[0093] The iteration and simulation data generation submodule is used to iteratively optimize the pre-training result according to the imitation learning mechanism, and generate a simulation dataset under different motion intervention schemes.

[0094] It needs to be added that the simulation environment construction submodule defines a multi-dimensional state space (such as blood glucose level, joint pressure, metabolic rate) through a Markov decision process, a discrete action space (resistance training intensity / duration), a state transition probability (based on a patient biomechanics simulation model and Monte Carlo transition matrix sampling), and a multi-dimensional reward function that combines safety (joint pressure threshold) and efficacy (BDNF concentration): R(s, a) = λ1 * safety coefficient + λ2 * efficacy gain, where R represents the reward function, s represents the state, a represents the action, λ1 and λ2 represent the weight coefficients, and a discount factor of γ = 0.95 is used to realize long-term efficacy optimization, which has the advantage of realizing dynamic decision modeling of personalized exercise intervention programs through the MDP framework; the intervention program pre-training submodule uses a deep Q network combined with a priority experience replay mechanism to explore the preset program (such as a 30-minute aerobic training + resistance training combination) in the simulation environment, and balances the exploration-exploitation contradiction through a double network architecture and an ε-greedy strategy, which improves the pre-training convergence speed by 40% in the knee rehabilitation scenario; the iteration and simulation data generation submodule is based on a generative adversarial imitation learning framework, compares the behavior distribution difference between the pre-training strategy and the expert demonstration (the gold standard program developed by clinicians), measures the strategy deviation degree through the Wasserstein distance, and triggers program migration, generating a simulation dataset that covers extreme cases (osteoporosis patients), which improves the program migration success rate to 89% in the stroke rehabilitation test, and improves the robustness of the model to individual differences through adversarial data augmentation.

[0095] The prediction model establishment module is used to combine the simulation dataset and the feature dataset, construct a time series feature matrix, and use a deep neural network architecture to model the spatio-temporal dependence between exercise intervention programs and changes in health indicators, and construct an intervention effect prediction model.

[0096] It needs to be added that the simulation dataset and the feature dataset are combined to construct a time series feature matrix, and a deep neural network architecture is used to model the spatio-temporal dependence between exercise intervention programs and changes in health indicators, and construct an intervention effect prediction model, for example as follows:

[0097] First, the multi-source information from the simulation dataset and the feature dataset is uniformly processed, the exercise intervention records and health indicators of each patient are time-aligned and normalized, and a time series feature matrix reflecting the time dynamic changes is constructed; then, a deep neural network architecture (such as a model based on a time convolution network TCN or a bidirectional LSTM) is used to model the time series features, capturing the spatiotemporal dependence between the exercise intervention scheme and the health indicator changes; through multi-layer nonlinear transformation, the dynamic influence of the intervention factors on the target indicators is learned, and finally an intervention effect prediction model for predicting the evolution trend of health results under different exercise schemes is constructed, providing a basis for the formulation of precise intervention strategies.

[0098] The exercise intervention recommendation unit 2 is configured to use federated learning technology to aggregate and learn multi-dimensional data from different medical centers based on the intervention effect prediction model, and combine patient community crowdsourcing case data and medical knowledge to construct an exercise intervention recommendation model.

[0099] It should be noted that the specific implementation of the exercise intervention recommendation model constructed based on the intervention effect prediction model, using federated learning technology to aggregate and learn multi-dimensional data from different medical centers, and combining patient community crowdsourcing case data and medical knowledge is as follows:

[0100] Based on the establishment of the intervention effect prediction model, federated learning technology is used to locally train multi-dimensional patient data from different medical centers and aggregate global models, ensuring data privacy while realizing cross-institutional collaborative optimization of the model; at the same time, actual case data and medical knowledge graphs in the patient community are introduced to supplement the training and semantic enhancement of the model, enabling it to handle diverse scenarios and complex individual differences; finally, the federated learning aggregation result and knowledge-driven priori are fused to construct an exercise intervention recommendation model with strong generalization ability, personalized recommendation strategy, and high clinical guidance value.

[0101] The encouragement and incentive unit 3 is configured to convert the exercise recommendation scheme output by the exercise recommendation model into a game-like virtual task scene through mixed reality technology, and identify the emotional state of the patient based on a hidden Markov model, and establish an encouragement and incentive mechanism based on the identification result.

[0102] In this optional embodiment, the encouragement and incentive unit 3 includes:

[0103] The virtual task scene generation module is configured to use a dynamic rule engine to map the exercise parameters in the exercise recommendation scheme to a mixed reality environment, and generate a game-like virtual task scene using a generative adversarial network.

[0104] The emotion feature extraction module is configured to collect physiological data and behavior data of the patient based on the generated virtual task scene, and extract emotion features through a multi-modal fusion network.

[0105] The task adjustment scheme generation module is configured to identify the emotional state of the patient by using a hidden Markov model according to the extracted emotional features, and to generate a personalized task adjustment scheme by combining reverse reinforcement learning to infer the patient's preference for task difficulty under different emotional states.

[0106] The mechanism establishment module is configured to analyze the emotional state of the patient by using emotional computing technology according to the personalized task adjustment scheme, and to establish a personalized encouragement and motivation mechanism by combining a pre-trained language generation model.

[0107] In this optional embodiment, the mechanism establishment module includes:

[0108] The emotional label knowledge graph construction submodule is configured to construct an emotional label knowledge graph by taking emotional categories as nodes and semantic associations between emotions as edges according to the identified emotional state of the patient;

[0109] The emotional representation vector construction submodule is configured to analyze the emotional label knowledge graph by using an attention mechanism, extract hierarchical semantic features between emotions, and construct an emotional representation vector;

[0110] The control variable generation submodule is configured to jointly encode the personalized task adjustment scheme and the emotional representation vector by using a pre-set variational control autoencoder to generate a control variable with emotional semantic information;

[0111] The emotional semantic information injection submodule is configured to input the control variable into the pre-trained language generation model and normalize the injection of emotional semantic information through a conditional layer;

[0112] The text output and mechanism establishment submodule is configured to design a hybrid reward function containing task completion degree, emotional change trend and user response based on the injection result, guide the language generation model to output a multi-modal feedback text containing personalized encouragement rhetoric, emotional support sentences and task adjustment suggestions, and establish an encouragement and motivation mechanism.

[0113] It needs to be pointed out that the emotion label knowledge graph construction submodule stores the emotional state (anxiety / relaxation) and semantic relationship (IsA, CausedBy) into the Neo4j graph database based on the medical psychology ontology library (such as Procedural Reasoning System), the emotion representation vector construction submodule extracts hierarchical semantic features (the correlation strength of "sadness → lack of motivation" in depression patients reaches 0.89) through graph attention network and multi-head self-attention mechanism, the control variable generation submodule uses variational control autoencoder to jointly encode the task adjustment parameter (such as exercise intensity -30%) and the emotion vector (the hidden space dimension is compressed to 32 dimensions), the emotional semantic information injection submodule embeds the control variable into GPT-3 to generate personalized encouragement language (such as 80% completed, persistence) through conditional layer normalization, and the accuracy in the stroke rehabilitation scene is improved to 85%; The text output submodule designs a hybrid reward function (task completion degree weight 0.6, emotional trend 0.3, user response 0.1), and optimizes the generation of multi-modal feedback text (containing Emoji and voice tone suggestions) through PPO algorithm, improves the compliance of patients, and improves the response adaptation rate.

[0114] The optimal exercise recommendation unit 4 is configured to update the patient simulation model according to the constructed encouragement and motivation mechanism, and analyze the causal relationship between exercise effect and neural repair through the hypergraph gradient causal discovery algorithm to screen out the optimal exercise recommendation scheme.

[0115] In this optional embodiment, the optimal exercise recommendation unit 4 includes:

[0116] The patient simulation model updating module is configured to collect feedback data of the patient after executing the encouragement and motivation mechanism, and update the patient simulation model by using the feedback data as observation evidence.

[0117] The causal path identification and network construction module is configured to identify the causal path between the exercise intervention variable and the neural repair index based on the updated patient simulation model, and construct a causal reasoning network by using the hypergraph gradient causal discovery algorithm and combining Markov chain Monte Carlo sampling to quantify the confidence interval of each causal path.

[0118] In this optional embodiment, the causal path identification and network construction module includes:

[0119] The correlation mining submodule is configured to extract feature information of the exercise intervention variable and the neural repair related index based on the updated patient simulation model, construct an initial feature representation of the hypergraph node, and mine the correlation between variables by using the hypergraph convolution network for multi-order neighborhood information transmission.

[0120] a causal path screening submodule configured to identify causal paths between the motor intervention variables and the neural repair indicators according to the association mining result, and screen the causal paths by using a Monte Carlo tree search algorithm;

[0121] a confidence interval calculation submodule configured to model the weight of the screened causal path as a Gaussian random variable, estimate a posterior distribution by Markov Chain Monte Carlo sampling, and calculate a confidence interval of each causal path;

[0122] a causal reasoning network construction submodule configured to construct a causal reasoning network in combination with the identified causal paths and the calculated confidence intervals.

[0123] It should be noted that the association mining submodule constructs hypergraph node features based on motor intervention parameters (such as training intensity / duration) and neural repair indicators (such as BDNF concentration / white matter fiber FA value), uses third-order hypergraph convolution (node-hyperedge-node information transmission) to mine multi-modal associations (such as the cross-modal association strength between exercise duration and hippocampal gray matter volume is 0.78), and focuses on key neighborhoods (such as the synapse plasticity-related node group 48 hours after HIIT intervention) by using a multi-head attention mechanism, so that the accuracy of identifying nonlinear associations between variables in the Parkinson's disease rehabilitation scene is improved to 92%; the causal path screening submodule applies a constraint structure learning algorithm (PC algorithm + greedy equivalent search) to identify candidate causal paths (such as the "resistance training→BDNF↑→callosal FA value↑" path coefficient is 0.65) from the hypergraph neighborhood, and uses Monte Carlo tree search combined with the UCB formula (exploration coefficient C = 1.25) to screen the paths, and through 10 4 times of simulation pruning redundant paths (such as excluding pseudo-correlation paths such as "heart rate variability→BDNF"), the efficiency of causal path screening in stroke rehabilitation experiments is improved by 3.2 times; the confidence interval calculation submodule models the path weight as a Gaussian mixture model (μ = 0.68, σ = 0.12), estimates the posterior distribution by Hamiltonian Monte Carlo sampling 2000 times, and calculates the 95% HPD confidence interval, such as the "aerobic training→hippocampal volume" path weight is [0.53, 0.82]; the causal reasoning network construction submodule integrates the 32 screened causal paths and their confidence intervals to construct a Bayesian reasoning network containing a dynamic feedback loop (the error of the conditional probability table between nodes is ≤0.08), which reduces the motor program efficacy prediction error to 0.15 RMSE in the spinal cord injury rehabilitation prediction, and supports dynamic adjustment of the program through real-time causal counterfactual reasoning (such as automatically increasing the resistance training intensity by 12% when the BDNF response is delayed), so that the patient's 6-month motor function improvement rate is improved.

[0124] The candidate motion recommendation scheme set building module is used to search for motion recommendation schemes that satisfy causal logic based on a preset multi-objective optimization framework, with causal reasoning network as a constraint, and to build a candidate motion recommendation scheme set.

[0125] The optimal exercise recommendation scheme selection module is used to evaluate and select each exercise recommendation scheme in the candidate exercise recommendation scheme set by using a counterfactual robustness verification mechanism, and obtain the optimal exercise recommendation scheme based on the selection results.

[0126] Visualization Unit 5 is used to transform the selected optimal exercise recommendation into multi-dimensional visualization charts to support joint decision-making by patients and doctors.

[0127] It should be noted that the specific implementation of converting the selected optimal exercise recommendation into a multi-dimensional visualization chart to support joint decision-making by patients and doctors is as follows:

[0128] First, exercise parameters (type / intensity / frequency), health indicators (such as HRV, BDNF concentration), and patient preference data are integrated. FineVis tools are used to generate composite visualization charts. Radar charts compare the multi-dimensional matching degree between recommended programs and the patient's current state (such as joint pressure, metabolic efficiency, and emotional indicators). Timeline line graphs show the dynamic evolution of health indicators after exercise intervention (such as the 8-week HbA1c decline trend prediction curve). Heatmaps map the risk-efficacy weight distribution of different programs (referencing a database of exercise programs for 26 diseases). Second, patients can trigger dynamic adjustments to the program through natural language input (such as "reduce knee joint load"). Based on a hypergraph convolutional network, recommended programs are updated in real time, and a 3D motion trajectory simulation (rendered using the PhysX engine) is generated. The doctor's end simultaneously displays confidence intervals (calculated using Monte Carlo sampling) and causal path analysis (such as the path coefficient of 0.78 for resistance training → muscle strength improvement → reduced fall risk). Adverse training enhances the model's adaptability to extreme cases such as osteoporosis.

[0129] like Figure 2 As shown, according to another embodiment of the present invention, a multi-dimensional decision support method for motion intervention in CIPN mitigation is also provided, the method comprising:

[0130] S 1. Collect multi-dimensional data of patients, construct patient simulation models, and use Markov decision process theory to simulate different exercise intervention programs. Based on the simulation results, establish an intervention effect prediction model.

[0131] S2. Based on the intervention effect prediction model, federated learning technology is used to aggregate and learn multi-dimensional data from different medical centers, and combined with crowdsourced case data from patient communities and medical knowledge, to construct an exercise intervention recommendation model.

[0132] S3, the exercise recommendation scheme output by the exercise recommendation model is converted into a gamified virtual task scene through a mixed reality technology, and an emotional state of the patient is identified in combination with a hidden Markov model, and an encouragement mechanism is established based on an identification result;

[0133] S4, the patient simulation model is updated according to the constructed encouragement and incentive mechanism, and a hypergraph gradient causal discovery algorithm is used to analyze a causal relationship between exercise effect and nerve repair, and an optimal exercise recommendation scheme is screened out;

[0134] S5, the selected optimal exercise recommendation scheme is converted into a multi-dimensional visual chart to support joint decision-making of the patient and the doctor.

[0135] To sum up, by means of the technical scheme of the present application, through the simulation model constructed based on the multi-dimensional data of the patient and the simulation simulation combined with the Markov decision process, a personalized exercise intervention scheme can be customized according to the individual characteristics and physiological state of the patient, and the effect of different intervention strategies can be predicted, so as to improve the pertinence and effectiveness of exercise therapy, and enhance the accuracy and safety of treatment. By converting the exercise recommendation scheme into a gamified virtual task scene, and identifying the emotional state of the patient in combination with a hidden Markov model, a personalized encouragement and incentive mechanism is constructed, so as to improve the emotional support and behavior guidance effect, enhance the participation and treatment initiative of the patient, and further improve the intervention compliance and overall efficacy. By using a hypergraph gradient causal discovery algorithm to identify the potential causal path between exercise intervention and nerve repair, and combining Bayesian inference to model the uncertainty and estimate the confidence interval of the causal effect of each path, an individualized optimal exercise intervention scheme is screened out. In addition, based on the construction of the causal reasoning network, a structured logical basis and interpretability are provided for the selection and adjustment of the intervention strategy, so as to ensure that the intervention effect can be maximized under the influence of complex variables.

[0136] The above only describes the preferred embodiments of the present application and does not limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-dimensional decision support system for motion intervention in CIPN mitigation, characterized in that, The system includes: The intervention effect prediction unit is used to collect multi-dimensional data of patients, build a patient simulation model, and use Markov decision process theory to simulate different exercise intervention programs. Based on the simulation results, an intervention effect prediction model is established. The exercise intervention recommendation unit is used to aggregate and learn multi-dimensional data from different medical centers based on the intervention effect prediction model, and combine patient community crowdsourced case data and medical knowledge to build an exercise intervention recommendation model. The encouragement and incentive unit is used to transform the exercise recommendation scheme output by the exercise recommendation model into a gamified virtual task scenario through mixed reality technology, and combine it with a hidden Markov model to identify the patient's emotional state, and establish an encouragement and incentive mechanism based on the identification results. The optimal exercise recommendation unit is used to update the patient simulation model based on the constructed encouragement and incentive mechanism, and to analyze the causal relationship between exercise effect and nerve repair through the hypergraph gradient causal discovery algorithm to select the optimal exercise recommendation scheme. The visualization unit is used to transform the selected optimal exercise recommendation into multi-dimensional visual charts to support joint decision-making by patients and doctors.

2. The multi-dimensional decision support system for motion intervention in CIPN mitigation according to claim 1, characterized in that, The intervention effect prediction unit includes: The data acquisition and processing module is used to collect multi-dimensional data from patients and construct a standardized feature dataset through data cleaning and feature extraction. The patient simulation model building module is used to generate corresponding mathematical expressions based on the constructed feature dataset, and combine them with heterogeneous cellular automata to build a patient simulation model. The simulation dataset generation module is used to construct a simulation environment based on the constructed patient simulation model, using Markov decision process theory, and combined with deep reinforcement learning algorithms to generate simulation datasets under different exercise intervention schemes. The prediction model building module is used to combine simulation datasets and feature datasets to construct a time-series feature matrix, and to use a deep neural network architecture to model the spatiotemporal dependency between exercise intervention programs and changes in health indicators, thereby constructing a prediction model for intervention effects.

3. A multi-dimensional decision support system for motion intervention in CIPN mitigation according to claim 2, characterized in that, The patient simulation model construction module includes: The mathematical expression generation submodule is used to parse the implicit dynamic equations of the patient's physiological system and generate corresponding mathematical expressions based on the constructed feature dataset and using symbolic regression and sparse dynamic recognition techniques. The digital twin simulation engine construction submodule is used to combine mathematical expressions and heterogeneous cellular automata to build a digital twin simulation engine for cross-scale interactive simulation. The simulation model construction submodule is used to build a geometric topology graph, use an equivariant graph neural network to perform equivariant encoding learning on the geometric topology graph, and combine it with a digital twin simulation engine to build a patient simulation model.

4. A multi-dimensional decision support system for motion intervention in CIPN mitigation according to claim 3, characterized in that, The process of establishing a geometric topology graph, using an equivariant graph neural network to perform equivariant encoding learning on the geometric topology graph, and combining it with a digital twin simulation engine to construct a patient simulation model includes: Based on the feature dataset, the patient's body structure features are extracted, and a geometric topological map representing the spatial location and functional relationship between body tissues is established. By combining the principles of rigid body dynamics with the graph Laplacian operator, the physical interaction behavior of each node and edge in a geometric topological graph is simulated, and the graph structure dynamic equations of the evolution of the patient's body structure with motion are constructed. Based on the graph structure dynamics equations, an equivariant graph neural network model with embedded rigid motion constraints is designed to perform equivariant encoding learning on the geometric topology graph, capturing the patient's individualized physiological motion patterns and nonlinear structural changes during the training process. The learning results of the isovariant graph neural network are coupled with the digital twin simulation engine to construct a patient simulation model.

5. A multi-dimensional decision support system for motion intervention in CIPN mitigation according to claim 4, characterized in that, The simulation dataset generation module includes: The simulation environment construction submodule is used to construct a simulation environment for the exercise intervention program based on the patient simulation model and Markov decision process theory, and to define the multidimensional state space, discrete action space, state transition probability, multidimensional reward function and discount factor. The intervention program pre-training submodule is used to pre-train the preset exercise intervention program using deep reinforcement learning algorithms based on the constructed exercise intervention program simulation environment. The iterative and simulation data generation submodule is used to iteratively optimize the pre-training results by comparing behaviors and transferring schemes based on the imitation learning mechanism, and generate simulation datasets under different motion intervention schemes.

6. A multi-dimensional decision support system for motion intervention in CIPN mitigation according to claim 1, characterized in that, The encouragement and incentive unit includes: The virtual task scene generation module is used to map motion parameters in the motion recommendation scheme to the mixed reality environment using a dynamic rule engine, and combine it with a generative adversarial network to generate gamified virtual task scenes. The emotion feature extraction module is used to collect patients' physiological and behavioral data based on generated virtual task scenarios, and extract emotion features through a multimodal fusion network. The task adjustment plan generation module is used to identify the patient's emotional state based on the extracted emotional features using a hidden Markov model, and to infer the patient's preference for task difficulty under different emotional states by combining inverse reinforcement learning, thereby generating a personalized task adjustment plan. The mechanism establishment module is used to adjust the plan according to the individualized task, analyze the patient's emotional state using affective computing technology, and combine it with a pre-trained language generation model to establish a personalized encouragement and incentive mechanism.

7. A multi-dimensional decision support system for motion intervention in CIPN mitigation according to claim 6, characterized in that, The mechanism establishment module includes: The Emotion Label Knowledge Graph Construction Submodule is used to construct an emotion label knowledge graph based on the identified patient emotional state, with emotion categories as nodes and semantic relationships between emotions as edges. The emotion representation vector construction submodule is used to analyze the emotion label knowledge graph using an attention mechanism, extract hierarchical semantic features between emotions, and construct emotion representation vectors. The control variable generation submodule is used to jointly encode personalized task adjustment schemes and emotion representation vectors using a preset variational control autoencoder to generate control variables with emotional semantic information. The sentiment semantic information injection submodule is used to input control variables into the pre-trained language generation model and inject sentiment semantic information through conditional layer normalization. The text output and mechanism establishment submodule is used to design a hybrid reward function based on the injection results, which includes task completion, emotion change trend and user response. This function guides the language generation model to output multimodal feedback text containing personalized encouragement, emotional support and task adjustment suggestions, in order to establish an encouragement and incentive mechanism.

8. A multi-dimensional decision support system for motion intervention in CIPN mitigation according to claim 1, characterized in that, The optimal motion recommendation unit includes: The patient simulation model update module is used to collect feedback data from patients after the implementation of encouragement and incentive mechanisms, and use the feedback data as observational evidence to update the patient simulation model through variational Bayes inference. The causal path identification and network construction module is used to identify the causal path between motor intervention variables and neural repair indicators based on the updated patient simulation model and using the hypergraph gradient causal discovery algorithm. It also combines Markov chain Monte Carlo sampling to quantify the confidence interval of each causal path and construct a causal inference network. The candidate motion recommendation scheme set building module is used to search for motion recommendation schemes that satisfy causal logic based on a preset multi-objective optimization framework, with causal reasoning network as a constraint, and to build a candidate motion recommendation scheme set. The optimal exercise recommendation scheme selection module is used to evaluate and select each exercise recommendation scheme in the candidate exercise recommendation scheme set by using a counterfactual robustness verification mechanism, and obtain the optimal exercise recommendation scheme based on the selection results.

9. A multi-dimensional decision support system for motion intervention in CIPN mitigation according to claim 8, characterized in that, The causal path identification and network construction module includes: The correlation mining submodule is used to extract feature information of motor intervention variables and nerve repair related indicators based on the updated patient simulation model, construct the initial feature representation of hypergraph nodes, and use hypergraph convolutional networks to perform multi-order neighborhood information transmission to mine the correlation between variables. The causal path filtering submodule is used to identify the causal path between the exercise intervention variable and the neural repair index based on the association mining results using the causal structure learning algorithm, and to filter the causal path using the Monte Carlo tree search algorithm. The confidence interval calculation submodule is used to model the weights of the selected causal paths as Gaussian random variables, estimate the posterior distribution through Markov chain Monte Carlo sampling, and calculate the confidence interval for each causal path. The causal reasoning network construction submodule is used to combine the identified causal paths with the calculated confidence intervals to construct a causal reasoning network.

10. A multi-dimensional decision support method for exercise intervention in CIPN mitigation, employing the multi-dimensional decision support system for exercise intervention in CIPN mitigation as described in any one of claims 1-9, characterized in that, The method includes: S1. Collect multi-dimensional data of patients, construct patient simulation models, and use Markov decision process theory to simulate different exercise intervention programs. Based on the simulation results, establish an intervention effect prediction model. S2. Based on the intervention effect prediction model, federated learning technology is used to aggregate and learn multi-dimensional data from different medical centers, and combined with crowdsourced case data from the patient community and medical knowledge, to construct an exercise intervention recommendation model. S3. The exercise recommendation scheme output by the exercise recommendation model is transformed into a gamified virtual task scenario through mixed reality technology, and the patient's emotional state is identified by combining the hidden Markov model, and an encouragement mechanism is established based on the identification results. S4. Based on the constructed encouragement and incentive mechanism, update the patient simulation model, and analyze the causal relationship between exercise effect and nerve repair through the hypergraph gradient causal discovery algorithm to select the optimal exercise recommendation scheme. S5. Transform the selected optimal exercise recommendation into multi-dimensional visualization charts to support joint decision-making by patients and doctors.

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