Medical full-course intelligent management system based on large model

Abstract

The invention discloses a medical whole-course intelligent management system based on a large model, and belongs to the technical field of large models. Comprising a multi-modal data acquisition module, a privacy calculation preprocessing module, a dynamic knowledge enhancement module, a time sequence data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module and a system security center module. The cross-mechanism data security sharing is realized, and the compliance of sensitive information processing is also ensured; a two-channel medical knowledge base is constructed, authoritative guidelines can be synchronized, newest clinical research data can be analyzed in real time, the knowledge base is kept in the newest state all the time, and the frontier scientific basis is provided for clinical decisions; dynamic modeling and trend prediction are carried out on long-term monitoring data of a patient by adopting a hybrid neural network model, and potential health risks and development trends can be identified more accurately.

Description

Technical Field

[0001] The present invention belongs to the technical field of large models, and specifically refers to an intelligent management system for the entire course of medical treatment based on large models. Background Art

[0002] With the rapid development of artificial intelligence technology, intelligent management systems for the entire medical process based on large models are gradually becoming a reality. The system uses deep learning and natural language processing technologies to integrate multiple data sources such as electronic health records, medical images, genetic data, and wearable device monitoring information to achieve comprehensive and personalized health management for patients. Data privacy and security are guaranteed through a federated learning framework, and dynamic knowledge graphs update the latest medical guidelines and research results in real time, combining reinforcement learning to optimize diagnosis and treatment strategies. In addition, the system also uses advanced time series data analysis models to predict disease development trends, and uses an intuitive human-computer interaction interface to improve the efficiency of doctor-patient communication;

[0003] However, the existing intelligent management of the entire medical course still has certain defects. The existing intelligent management of the entire medical course uses a dual-channel medical knowledge base to synchronize authoritative guidelines and real-time analysis of clinical research data. However, due to the rapid development of medical research, how to ensure that all the latest discoveries can be incorporated into the knowledge base in a timely and accurate manner remains a challenge; when using a hybrid neural network model for long-term monitoring data analysis, problems such as low data quality and complex processing of non-stationary sequences may be encountered; the method of assessing patient compliance and adjusting treatment plans based on Bayesian networks relies on a large amount of historical data and personal behavior patterns, and may not respond quickly enough to new situations; although blockchain evidence storage and RBAC permission models are used to ensure system security, as attack methods continue to evolve, existing protection measures may not be sufficient to resist new threats. For this reason, a large-scale model-based intelligent management system for the entire medical course is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a large-scale model-based intelligent management system for the entire medical course to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a large-scale model-based intelligent management system for the entire medical course, comprising a multimodal data acquisition module, a privacy computing preprocessing module, a dynamic knowledge enhancement module, a time series data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module, and a system security hub module;

[0006] The multimodal data acquisition module is used to integrate medical images, wearable devices and electronic medical record data to achieve standardized access and analysis of multi-source heterogeneous medical information;

[0007] The privacy computing preprocessing module is used to achieve cross-institutional data security sharing based on the federated learning framework and ensure the compliance of sensitive information processing through differential privacy technology;

[0008] The dynamic knowledge enhancement module is used to build a dual-channel medical knowledge base, synchronizing authoritative guidelines with clinical research data analyzed in real time by large models;

[0009] The time series data analysis module is used to dynamically model and predict trends of patients' long-term monitoring data using a hybrid neural network model;

[0010] The intelligent decision engine module is used to integrate reinforcement learning and causal reasoning algorithms to generate personalized multimodal diagnosis and treatment plans;

[0011] The multidisciplinary collaboration module is used to achieve cross-disciplinary collaborative decision-making and conflict arbitration through a hierarchical intelligent agent architecture;

[0012] The patient interaction platform module is used to provide a virtual assistant for natural language interaction and 3D visual health education content;

[0013] The dynamic intervention feedback module is used to evaluate patient compliance in real time and optimize intervention strategies based on a Bayesian network;

[0014] The system security hub module is used to implement full-process security management and control using blockchain evidence storage and RBAC permission model.

[0015] Among them, the deployment of DICOM protocol parsing engine, development of DICOM Tag parser, extraction of key metadata, construction of IoT protocol adaptation layer, design of equipment manufacturer SDK packaging framework, development of FHIR standard interface, docking with hospital HIS / EMR system, deployment of medical-specific OCR engine for unstructured text; construction of unified spatiotemporal coordinate system, data synchronization through timestamp calibration and device location information, construction of medical data feature fingerprint library, automatic identification of data source type through machine learning, and use of graph database to store data lineage relationship.

[0016] Among them, the central coordination node is deployed, and each medical institution registers with the coordination node as a participant, submits a digital certificate to verify its identity, establishes an OAuth 2.0-based permission management system, and defines data access roles; locates direct identifiers through regular expressions and named entity recognition models, generalizes indirect identifiers, divides data sets according to feature dimensions, and encrypts local data using homomorphic encryption; participants train the initial model locally, use secure multi-party computing technology to encrypt model parameters, and upload the encrypted parameters to the coordination node through the TLS1.3 encrypted channel. The coordination node performs aggregation operations and integrates the parameters of each participant; adopts an adaptive budget consumption strategy to calculate the cumulative privacy loss in real time; records all data access and model call logs, stores them in the blockchain, and verifies data usage compliance through zero-knowledge proof.

[0017] Among them, the integrated structured terminology library is imported through API, a medical ontology mapping engine is built, scheduled tasks are set to detect guideline updates, the latest clinical trial summaries are captured, the hospital scientific research database is connected, and Apache Kafka is used to build a real-time data pipeline; a rule-based parser is developed to extract structured fields in the guidelines, a decision tree model is built, the guideline recommendation strength is converted into computable logic, and a confidence-based conflict resolution algorithm is designed; core relationships are defined based on the UMLS semantic network and specific clinical data are attached; descriptive logic is applied to detect logical contradictions in the graph, and AI-generated knowledge nodes are hierarchically annotated, and the annotation results are fed back to the large model for fine-tuning.

[0018] Among them, the missing data caused by sensor interruption is filled by time series interpolation data, long-term missing segments are marked as invalid intervals and trigger data source abnormality alarms, blood glucose, blood pressure, and heart rate multimodal monitoring data are aligned based on a unified timestamp, a time offset compensation algorithm is constructed to eliminate clock deviations between devices, a sliding window mechanism is used to generate training samples, and the energy proportion of the main frequency component is obtained through FFT conversion, and differential stabilization is performed on non-stationary sequences; a bidirectional LSTM network is deployed to capture local temporal dependencies, a time attention gating mechanism is set, and the feature weights of key time points are enhanced, a multi-head self-attention mechanism is used to model long-term dependencies across windows, potential abnormal events are identified based on reconstruction errors and dynamic thresholds, and the SHAP value is used to quantify the contribution of each feature to the prediction results; daily new patient data triggers model fine-tuning, elastic weight solidification is used, and the predicted value distribution is output based on the Bayesian neural network, a red alert is triggered for high-risk predictions, the model prediction results are compared with the medical knowledge base rules, and a feedback data set is constructed.

[0019] Among them, the integration of multimodal data generates patient feature vectors, uses Embedding technology to map discrete features into low-dimensional dense vectors, constructs a feature importance screening model to filter redundant features, retains key dimensions, and defines the medical decision state space; constructs a dynamic causal graph based on the medical knowledge graph, estimates potential intervention effects through counterfactual analysis, uses deep deterministic policy gradients to select treatment targets, and generates specific diagnosis and treatment actions based on proximal policy optimization; generates CDA documents that comply with the HL7 standard, uses GNN visualization tools to display the association path of the treatment plan in the knowledge graph, and adjusts the strategy in real time based on patient compliance feedback; constructs a rule engine verification layer, intercepts abnormal decisions, and calculates the decision variance based on Bootstrap sampling. The implementation formula is:

[0020] Scalar decision variance formula:

[0021]

[0022] Vector decision covariance rectangle formula:

[0023]

[0024] B represents the number of Bootstrap sampling, D b Indicates the decision result generated by the b-th Bootstrap sampling, It represents the mean of all Bootstrap sample decision results, Var(D) represents the decision variance, which measures the volatility of different Bootstrap sample decision results, Cov(D) represents the decision covariance matrix, and the diagonal elements are the variances of each dimension. B-1 represents the degrees of freedom in the denominator, which is used for unbiased estimation of sample variance.

[0025] Among them, the system connects to the multimodal data acquisition module, cleans specialty data, performs term mapping, triggers threshold alarms based on the rule engine, adopts a graph-based reasoning engine, generates preliminary diagnosis and treatment recommendations that comply with specialty guidelines, and dynamically adjusts weights based on the department's historical treatment success rate; designs inter-specialty data exchange specifications, builds a real-time communication framework based on gRPC, optimizes resource scheduling based on the Hungarian algorithm, and minimizes consultation delays; records decision chain data, and uses blockchain to store tamper-proof decision logs; and applies natural language generation technology to convert multidisciplinary disagreements into clinically readable reports.

[0026] Among them, the integrated medical field dictionary improves the accuracy of proper noun recognition, adopts an emotional speech engine, dynamically adjusts the timbre according to the content type, builds a medical intent classification model, defines patient intent, and applies the BERT-Med model to improve the accuracy of intent recognition; adopts a graph neural network to model the conversation state, and dynamically adjusts the content based on the patient portrait; constructs an organ-level three-dimensional anatomical model, adopts CT / MRI image reconstruction technology to generate a high-precision model, and develops interactive surgical simulation; develops a unified session management service, and adopts WebRTC technology to achieve real-time data synchronization; develops tactile feedback gloves, converts 3D model structures into vibration coding signals, and the brain-computer interface supports severely paralyzed patients to select educational content through brain waves.

[0027] Among them, the multi-source data integration constructs a unified timeline, maps fragmented data to a standard time series template, and uses entity resolution technology to eliminate cross-device data identity ambiguity; establishes a causal chain of health education exposure → medication knowledge → compliance, defines previous breach records → current compliance attenuation factors, uses the EM algorithm to learn conditional probability tables based on historical data, adopts the Bayesian update rule, updates network parameters according to preset time increments, and triggers immediate parameter adjustments for sudden abnormal data; uses variable elimination method to calculate accurate posterior probabilities for key decision scenarios, uses random walk sampling for rapid estimation of real-time streaming data, and constructs a three-dimensional risk assessment matrix; estimates the potential compliance of unimplemented interventions, automatically converts successful intervention cases into rules, and triggers Bayesian network structure adjustments for failed cases, and updates global model parameters monthly through security aggregation; displays patient compliance heat maps on a real-time dashboard, provides intervention measure simulation and deduction tools, visualizes the compliance health index progress bar, and generates personalized intervention roadmaps.

[0028] Among them, the scope of data on the chain is defined, key operations and high-risk behaviors are handled by a consortium chain architecture, medical institutions are set as consensus nodes, regulatory agencies are set as verification nodes, dedicated channels are designed to isolate different data types, Merkle tree root hash is calculated for unstructured data, and a unique identifier is generated for structured data using the SHA-3 algorithm; role permissions are automatically upgraded and downgraded based on behavioral data, a temporary permission application mechanism for high-risk operations is implemented, and access rights during non-working hours are restricted; an LSTM-based baseline behavioral model is constructed to detect deviation operation sequences, a rule engine is used to detect high-risk combinations, and the MITRE ATT&CK framework is used to model attack patterns unique to the medical industry; the PBFT algorithm is improved to reduce consensus delays from seconds to milliseconds.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. This invention utilizes a federated learning framework and differential privacy technology to not only achieve secure cross-institutional data sharing but also ensure compliance in the handling of sensitive information. It also builds a dual-channel medical knowledge base that synchronizes authoritative guidelines and analyzes the latest clinical research data in real time, ensuring that the knowledge base remains up-to-date and providing cutting-edge scientific evidence for clinical decision-making. It also employs a hybrid neural network model to dynamically model and predict trends in long-term patient monitoring data, enabling more accurate identification of potential health risks and development trends.

[0031] 2. This invention uses a hierarchical agent architecture to achieve cross-specialty collaborative decision-making and conflict resolution, simplifying the complex interdisciplinary communication process and improving the overall efficiency and quality of medical services. The natural language interactive virtual assistant and 3D visual health education resources provided by the invention enhance the interaction between doctors and patients, improving patient engagement and education levels.

[0032] 3. This invention further improves the treatment effect by evaluating patient compliance in real time and optimizing intervention strategies based on Bayesian networks, and adjusting treatment plans according to actual feedback;

[0033] 4. This invention ensures the security of the entire system by using blockchain evidence and RBAC permission model. All key operations are recorded and their compliance is verified through zero-knowledge proof, which greatly enhances the transparency and trust of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic diagram of the structure of the intelligent management system for the entire course of medical treatment based on a large model of the present invention;

[0035] Figure 2 This is the operating process of the medical full course intelligent management system based on the large model of the present invention Figure 1 ;

[0036] Figure 3 This is the operating process of the medical full course intelligent management system based on the large model of the present invention Figure 2 . DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] Example

[0039] See also Figure 1-Figure 3As shown, the present invention provides a technical solution: including a multimodal data acquisition module, a privacy computing preprocessing module, a dynamic knowledge enhancement module, a time series data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction middle platform module, a dynamic intervention feedback module, and a system security hub module;

[0040] The multimodal data acquisition module is used to integrate medical images, wearable devices and electronic medical record data to achieve standardized access and analysis of multi-source heterogeneous medical information;

[0041] The privacy computing preprocessing module is used to achieve cross-institutional data security sharing based on the federated learning framework and ensure the compliance of sensitive information processing through differential privacy technology;

[0042] The dynamic knowledge enhancement module is used to build a dual-channel medical knowledge base, synchronizing authoritative guidelines with clinical research data analyzed in real time by large models;

[0043] The time series data analysis module is used to dynamically model and predict trends of patients' long-term monitoring data using a hybrid neural network model;

[0044] The intelligent decision engine module is used to integrate reinforcement learning and causal reasoning algorithms to generate personalized multimodal diagnosis and treatment plans;

[0045] The multidisciplinary collaboration module is used to achieve cross-disciplinary collaborative decision-making and conflict arbitration through a hierarchical intelligent agent architecture;

[0046] The patient interaction platform module is used to provide a virtual assistant for natural language interaction and 3D visual health education content;

[0047] The dynamic intervention feedback module is used to evaluate patient compliance in real time and optimize intervention strategies based on a Bayesian network;

[0048] The system security hub module is used to implement full-process security management and control using blockchain evidence storage and RBAC permission model.

[0049] Among them, the deployment of DICOM protocol parsing engine, development of DICOM Tag parser, extraction of key metadata, construction of IoT protocol adaptation layer, design of equipment manufacturer SDK packaging framework, development of FHIR standard interface, docking with hospital HIS / EMR system, deployment of medical-specific OCR engine for unstructured text; construction of unified spatiotemporal coordinate system, data synchronization through timestamp calibration and device location information, construction of medical data feature fingerprint library, automatic identification of data source type through machine learning, and use of graph database to store data lineage relationship.

[0050] Among them, the central coordination node is deployed, and each medical institution registers with the coordination node as a participant, submits a digital certificate to verify its identity, establishes an OAuth 2.0-based permission management system, and defines data access roles; locates direct identifiers through regular expressions and named entity recognition models, generalizes indirect identifiers, divides data sets according to feature dimensions, and encrypts local data using homomorphic encryption; participants train the initial model locally, use secure multi-party computing technology to encrypt model parameters, and upload the encrypted parameters to the coordination node through the TLS1.3 encrypted channel. The coordination node performs aggregation operations and integrates the parameters of each participant; adopts an adaptive budget consumption strategy to calculate the cumulative privacy loss in real time; records all data access and model call logs, stores them in the blockchain, and verifies data usage compliance through zero-knowledge proof.

[0051] Among them, the integrated structured terminology library is imported through API, a medical ontology mapping engine is built, scheduled tasks are set to detect guideline updates, the latest clinical trial summaries are captured, the hospital scientific research database is connected, and Apache Kafka is used to build a real-time data pipeline; a rule-based parser is developed to extract structured fields in the guidelines, a decision tree model is built, the guideline recommendation strength is converted into computable logic, and a confidence-based conflict resolution algorithm is designed; core relationships are defined based on the UMLS semantic network and specific clinical data are attached; descriptive logic is applied to detect logical contradictions in the graph, and AI-generated knowledge nodes are hierarchically annotated, and the annotation results are fed back to the large model for fine-tuning.

[0052] Among them, the missing data caused by sensor interruption is filled by time series interpolation data, long-term missing segments are marked as invalid intervals and trigger data source abnormality alarms, blood glucose, blood pressure, and heart rate multimodal monitoring data are aligned based on a unified timestamp, a time offset compensation algorithm is constructed to eliminate clock deviations between devices, a sliding window mechanism is used to generate training samples, and the energy proportion of the main frequency component is obtained through FFT conversion, and differential stabilization is performed on non-stationary sequences; a bidirectional LSTM network is deployed to capture local temporal dependencies, a time attention gating mechanism is set, and the feature weights of key time points are enhanced, a multi-head self-attention mechanism is used to model long-term dependencies across windows, potential abnormal events are identified based on reconstruction errors and dynamic thresholds, and the SHAP value is used to quantify the contribution of each feature to the prediction results; daily new patient data triggers model fine-tuning, elastic weight solidification is used, and the predicted value distribution is output based on the Bayesian neural network, a red alert is triggered for high-risk predictions, the model prediction results are compared with the medical knowledge base rules, and a feedback data set is constructed.

[0053] Among them, the integration of multimodal data generates patient feature vectors, uses Embedding technology to map discrete features into low-dimensional dense vectors, constructs a feature importance screening model to filter redundant features, retains key dimensions, and defines the medical decision state space; constructs a dynamic causal graph based on the medical knowledge graph, estimates potential intervention effects through counterfactual analysis, uses deep deterministic policy gradients to select treatment targets, and generates specific diagnosis and treatment actions based on proximal policy optimization; generates CDA documents that comply with the HL7 standard, uses GNN visualization tools to display the association path of the treatment plan in the knowledge graph, and adjusts the strategy in real time based on patient compliance feedback; constructs a rule engine verification layer, intercepts abnormal decisions, and calculates the decision variance based on Bootstrap sampling. The implementation formula is:

[0054] Scalar decision variance formula:

[0055]

[0056] Vector decision covariance rectangle formula:

[0057]

[0058] B represents the number of Bootstrap sampling, D b Indicates the decision result generated by the b-th Bootstrap sampling, It represents the mean of all Bootstrap sample decision results, Var(D) represents the decision variance, which measures the volatility of different Bootstrap sample decision results, Cov(D) represents the decision covariance matrix, and the diagonal elements are the variances of each dimension. B-1 represents the degrees of freedom in the denominator, which is used for unbiased estimation of sample variance.

[0059] Among them, the system connects to the multimodal data acquisition module, cleans specialty data, performs term mapping, triggers threshold alarms based on the rule engine, adopts a graph-based reasoning engine, generates preliminary diagnosis and treatment recommendations that comply with specialty guidelines, and dynamically adjusts weights based on the department's historical treatment success rate; designs inter-specialty data exchange specifications, builds a real-time communication framework based on gRPC, optimizes resource scheduling based on the Hungarian algorithm, and minimizes consultation delays; records decision chain data, and uses blockchain to store tamper-proof decision logs; and applies natural language generation technology to convert multidisciplinary disagreements into clinically readable reports.

[0060] Among them, the integrated medical field dictionary improves the accuracy of proper noun recognition, adopts an emotional speech engine, dynamically adjusts the timbre according to the content type, builds a medical intent classification model, defines patient intent, and applies the BERT-Med model to improve the accuracy of intent recognition; adopts a graph neural network to model the conversation state, and dynamically adjusts the content based on the patient portrait; constructs an organ-level three-dimensional anatomical model, adopts CT / MRI image reconstruction technology to generate a high-precision model, and develops interactive surgical simulation; develops a unified session management service, and adopts WebRTC technology to achieve real-time data synchronization; develops tactile feedback gloves, converts 3D model structures into vibration coding signals, and the brain-computer interface supports severely paralyzed patients to select educational content through brain waves.

[0061] Among them, the multi-source data integration constructs a unified timeline, maps fragmented data to a standard time series template, and uses entity resolution technology to eliminate cross-device data identity ambiguity; establishes a causal chain of health education exposure → medication knowledge → compliance, defines previous breach records → current compliance attenuation factors, uses the EM algorithm to learn conditional probability tables based on historical data, adopts the Bayesian update rule, updates network parameters according to preset time increments, and triggers immediate parameter adjustments for sudden abnormal data; uses variable elimination method to calculate accurate posterior probabilities for key decision scenarios, uses random walk sampling for rapid estimation of real-time streaming data, and constructs a three-dimensional risk assessment matrix; estimates the potential compliance of unimplemented interventions, automatically converts successful intervention cases into rules, and triggers Bayesian network structure adjustments for failed cases, and updates global model parameters monthly through security aggregation; displays patient compliance heat maps on a real-time dashboard, provides intervention measure simulation and deduction tools, visualizes the compliance health index progress bar, and generates personalized intervention roadmaps.

[0062] Among them, the scope of data on the chain is defined, key operations and high-risk behaviors are handled by a consortium chain architecture, medical institutions are set as consensus nodes, regulatory agencies are set as verification nodes, dedicated channels are designed to isolate different data types, Merkle tree root hash is calculated for unstructured data, and a unique identifier is generated for structured data using the SHA-3 algorithm; role permissions are automatically upgraded and downgraded based on behavioral data, a temporary permission application mechanism for high-risk operations is implemented, and access rights during non-working hours are restricted; an LSTM-based baseline behavioral model is constructed to detect deviation operation sequences, a rule engine is used to detect high-risk combinations, and the MITRE ATT&CK framework is used to model attack patterns unique to the medical industry; the PBFT algorithm is improved to reduce consensus delays from seconds to milliseconds.

[0063] Working principle: By integrating data from different sources, these multi-source heterogeneous information can be accessed and parsed in a standardized manner. Based on the federated learning framework, it allows secure data sharing across institutions, while using differential privacy technology to protect sensitive information and conduct data analysis; a dual-channel medical knowledge base is built to synchronize authoritative guidelines and analyze clinical research data in real time; a hybrid neural network model is used to focus on dynamic modeling and trend prediction of patients' long-term monitoring data; through in-depth analysis of time series data, it helps predict the changing trends of patients' health status; it integrates reinforcement learning and causal reasoning algorithms to generate personalized diagnosis and treatment plans; through a hierarchical intelligent body architecture, collaborative decision-making and conflict resolution mechanisms between specialties are achieved; a virtual assistant with natural language interaction and 3D visual health education resources are provided to enhance communication between doctors and patients; based on the Bayesian network, this module can evaluate patient compliance in real time and optimize intervention strategies accordingly; blockchain technology and RBAC permission model are used to ensure system security, all key operations are recorded, and their compliance is verified through zero-knowledge proof.

[0064] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0065] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A large-scale model-based intelligent management system for the entire medical process, characterized by: It includes multimodal data acquisition module, privacy computing preprocessing module, dynamic knowledge enhancement module, time series data analysis module, intelligent decision engine module, multidisciplinary collaboration module, patient interaction platform module, dynamic intervention feedback module, and system security center module; The multimodal data acquisition module is used to integrate medical images, wearable devices and electronic medical record data to achieve standardized access and analysis of multi-source heterogeneous medical information; The privacy computing preprocessing module is used to achieve cross-institutional data security sharing based on the federated learning framework and ensure the compliance of sensitive information processing through differential privacy technology; The dynamic knowledge enhancement module is used to build a dual-channel medical knowledge base, synchronizing authoritative guidelines with clinical research data analyzed in real time by large models; The time series data analysis module is used to dynamically model and predict trends of patients' long-term monitoring data using a hybrid neural network model; The intelligent decision engine module is used to integrate reinforcement learning and causal reasoning algorithms to generate personalized multimodal diagnosis and treatment plans; The multidisciplinary collaboration module is used to achieve cross-disciplinary collaborative decision-making and conflict arbitration through a hierarchical intelligent agent architecture; The patient interaction platform module is used to provide a virtual assistant for natural language interaction and 3D visual health education content; The dynamic intervention feedback module is used to evaluate patient compliance in real time and optimize intervention strategies based on a Bayesian network; The system security hub module is used to implement full-process security management and control using blockchain evidence storage and RBAC permission model.

2. The large-scale model-based intelligent management system for the entire course of medical treatment according to claim 1 is characterized by: The method involves deploying a DICOM protocol parsing engine, extracting key metadata through the parser, building an IoT protocol adaptation layer, designing an equipment manufacturer SDK encapsulation framework, developing a FHIR standard interface, connecting to the hospital HIS / EMR system, and deploying a medical-specific OCR engine for unstructured text. The method also involves building a unified spatiotemporal coordinate system, achieving data synchronization through timestamp calibration and device location information, building a medical data feature fingerprint library, automatically identifying data source types through machine learning, and using a graph database to store data lineage relationships.

3. The large-scale model-based intelligent management system for the entire course of medical treatment according to claim 1 is characterized by: The central coordination node is deployed, and each medical institution registers with the coordination node as a participant, submits a digital certificate to verify its identity, establishes a permission management system, and defines data access roles; locates direct identifiers through regular expressions and named entity recognition models, generalizes indirect identifiers, divides data sets according to feature dimensions, and encrypts local data using homomorphic encryption; participants train the initial model locally, use secure multi-party computing technology to encrypt model parameters, and upload the encrypted parameters to the coordination node through the TLS1.3 encrypted channel. The coordination node performs aggregation operations and integrates the parameters of each participant; adopts an adaptive budget consumption strategy to calculate the cumulative privacy loss in real time; records all data access and model call logs, stores them in the blockchain, and verifies data usage compliance through zero-knowledge proof.

4. The large-scale model-based intelligent management system for the entire course of medical treatment according to claim 1 is characterized by: The integrated structured terminology library is imported through an API to build a medical ontology mapping engine, set up scheduled tasks to detect guideline updates, capture the latest clinical trial summaries, and use Apache Kafka to build a real-time data pipeline; develop a rule-based parser, extract structured fields in the guidelines, build a decision tree model, convert the guideline recommendation strength into computable logic, and design a confidence-based conflict resolution algorithm; define core relationships based on the UMLS semantic network and attach specific clinical data; apply description logic to detect logical contradictions in the graph, perform hierarchical annotation of AI-generated knowledge nodes, and feed the annotation results back to the large model for fine-tuning.

5. The large-scale model-based intelligent management system for the entire course of medical treatment according to claim 1 is characterized by: The missing data caused by sensor interruption is filled with time series interpolation data. Long-term missing segments are marked as invalid intervals and trigger data source abnormality alarms. Blood glucose, blood pressure, and heart rate multimodal monitoring data are aligned based on a unified timestamp. A time offset compensation algorithm is constructed to eliminate clock deviations between devices. A sliding window mechanism is used to generate training samples. The energy proportion of the main frequency component is obtained through FFT conversion, and differential stabilization processing is performed on non-stationary sequences. Deploy a bidirectional LSTM network to capture local temporal dependencies, set up a temporal attention gating mechanism, enhance the feature weights of key time points, adopt a multi-head self-attention mechanism to model long-term dependencies across windows, identify potential abnormal events based on reconstruction error and dynamic thresholds, and apply SHAP values to quantify the contribution of each feature to the prediction results; New patient data added daily triggers model fine-tuning, adopts elastic weight solidification, outputs predicted value distribution based on Bayesian neural network, triggers red alert for high-risk prediction, compares model prediction results with medical knowledge base rules, and constructs feedback data set.

6. The large-scale model-based intelligent management system for the entire course of medical treatment according to claim 1 is characterized by: The multimodal data is integrated to generate a patient feature vector, and the embedding technology is used to map discrete features into low-dimensional dense vectors. A feature importance screening model is constructed to filter redundant features, retain key dimensions, and define a medical decision state space. Based on the medical knowledge graph, a dynamic causal graph is constructed. The potential intervention effect is estimated through counterfactual analysis. The treatment target is selected using a deep deterministic policy gradient. Specific diagnosis and treatment actions are generated based on proximal policy optimization. CDA documents that comply with the HL7 standard are generated. The GNN visualization tool is used to display the association path of the treatment plan in the knowledge graph. The strategy is adjusted in real time based on patient compliance feedback. A rule engine verification layer is constructed to intercept abnormal decisions. The decision variance is calculated based on Bootstrap sampling. The implementation formula is as follows: Scalar decision variance formula: Vector decision covariance rectangle formula: B represents the number of Bootstrap sampling, D b Indicates the decision result generated by the b-th Bootstrap sampling, It represents the mean of all Bootstrap sample decision results, Var(D) represents the decision variance, which measures the volatility of different Bootstrap sample decision results, Cov(D) represents the decision covariance matrix, and the diagonal elements are the variances of each dimension. B-1 represents the degrees of freedom in the denominator, which is used for unbiased estimation of sample variance.

7. The large-scale model-based intelligent management system for the entire course of medical treatment according to claim 1 is characterized by: The said docking multimodal data acquisition module cleans specialist data, performs term mapping, triggers threshold alarms based on the rule engine, and uses a graph-based reasoning engine to generate preliminary diagnosis and treatment recommendations that comply with specialist guidelines, dynamically adjusting weights based on the department's historical treatment success rate; Design inter-specialty data exchange specifications, build a real-time communication framework based on gRPC, optimize resource scheduling based on the Hungarian algorithm, and minimize consultation delays; record decision chain data and use blockchain to store tamper-proof decision logs; apply natural language generation technology to convert multidisciplinary disagreements into clinically readable reports.

8. The large-scale model-based intelligent management system for the entire course of medical treatment according to claim 1 is characterized by: The integrated medical field dictionary improves the accuracy of proper noun recognition, adopts an emotional speech engine, dynamically adjusts the timbre according to the content type, builds a medical intent classification model, defines patient intent, and applies the BERT-Med model to improve the accuracy of intent recognition; uses a graph neural network to model conversation status, and dynamically adjusts content based on patient portraits; builds an organ-level three-dimensional anatomical model, uses CT / MRI image reconstruction technology to generate high-precision models, and develops interactive surgical simulations; develops a unified session management service, and uses WebRTC technology to achieve real-time data synchronization; develops tactile feedback gloves to convert 3D model structures into vibration coding signals, and the brain-computer interface supports severely paralyzed patients to select educational content through brain waves.

9. The large-scale model-based intelligent management system for the entire course of medical treatment according to claim 1 is characterized by: The multi-source data integration constructs a unified timeline, maps fragmented data to a standard time series template, and uses entity resolution technology to eliminate cross-device data identity ambiguity; establishes a causal chain from health education exposure → medication knowledge → compliance, defines a previous default record → current compliance attenuation factor, uses the EM algorithm to learn a conditional probability table based on historical data, adopts the Bayesian update rule, updates network parameters according to preset time increments, and triggers immediate parameter adjustments for sudden abnormal data; For key decision-making scenarios, variable elimination is used to calculate the precise posterior probability, and random walk sampling is used to quickly estimate real-time streaming data to construct a three-dimensional risk assessment matrix. The potential compliance of patients without intervention is estimated, successful intervention cases are automatically converted into rules, and failure cases trigger Bayesian network structure adjustments. Global model parameters are updated monthly through security aggregation. A real-time dashboard displays a patient compliance heat map, provides intervention measure simulation and deduction tools, visualizes the compliance health index progress bar, and generates a personalized intervention roadmap.

10. The large-scale model-based intelligent management system for the entire course of medical treatment according to claim 1 is characterized by: The scope of data on-chain is defined, including key operations and high-risk behaviors. A consortium chain architecture is adopted, with medical institutions set as consensus nodes and regulatory agencies as verification nodes. Dedicated channels are designed to isolate different data types, Merkle tree root hash is calculated for unstructured data, and a unique identifier is generated for structured data using the SHA-3 algorithm. Role permissions are automatically upgraded and downgraded based on behavioral data, and a temporary permission application mechanism for high-risk operations is established to restrict access rights during non-working hours. A baseline behavioral model based on LSTM is constructed to detect deviation operation sequences, a rule engine is used to detect high-risk combinations, and the MITRE ATT&CK framework is used to model attack patterns unique to the medical industry. The PBFT algorithm is improved to reduce consensus latency from seconds to milliseconds.

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