Medical platform management system

Abstract

The invention provides a medical platform management system, and relates to the technical field of medical health. Comprising a multi-source heterogeneous data intelligent acquisition module, the multi-source heterogeneous data intelligent acquisition module is connected with an external system through a distributed data acquisition framework and collects and integrates multi-source heterogeneous data, and the multi-source heterogeneous data comprises but is not limited to medical text data, multi-modal medical image data and real-time medical sensor data; aiming at the problem, the multi-source heterogeneous data intelligent acquisition module designs an intelligent data interface adapter, supports multiple medical data transmission protocols such as RESTAPI, HL7FHIR, DICOM and the like, realizes data standardized mapping through an efficient data conversion engine, solves the problem of cross-mechanism and cross-system data intercommunication in a medical body, and improves the data transmission efficiency. And a foundation is laid for realizing real medical integration.

Description

Technical Field

[0001] This application relates to the field of medical and health technology, and in particular to a medical community platform management system. Background Art

[0002] During the development of traditional medical communities, heterogeneity and compatibility issues are common among information systems at all levels of medical institutions. Primary healthcare institutions, regional hospitals, and specialized hospitals often use medical information systems developed by different vendors, with varying data standards and inconsistent interface protocols. This hinders the effective circulation and sharing of medical data. In particular, the lack of unified data interface standards and data quality control mechanisms in the data collection process makes it difficult to integrate heterogeneous data from multiple sources, including public health management systems, hospital information systems, and family doctor contract systems.

[0003] Existing medical information systems primarily focus on the storage and analysis of structured data, but have limited processing capabilities for unstructured data, which accounts for over 80% of medical data, such as doctors' handwritten medical records and text descriptions in imaging reports. Traditional methods rely heavily on manual reading and interpretation, which is inefficient and error-prone. This makes it difficult to extract valuable medical information from massive amounts of unstructured medical text, hindering the accuracy and comprehensiveness of medical decision-making.

[0004] Traditional medical information systems generally employ single-modality data analysis methods, making it difficult to integrate heterogeneous medical data from multiple sources, including text, images, and sensors, for comprehensive analysis. Furthermore, existing systems focus primarily on assessing current health status and lack the ability to predict future disease trends, resulting in insufficient scientific evidence for preventive medical interventions. Furthermore, traditional medical analysis models are often static, based on statistical methods, making it difficult to capture the dynamic nature of disease progression and the complex relationships between medical entities.

[0005] In view of this, we proposed a medical community platform management system. Summary of the Invention

[0006] In order to overcome a series of defects in the existing technology, the purpose of this application is to provide a medical community platform management system to address the above problems, and the system includes the following modules.

[0007] The multi-source heterogeneous data intelligent acquisition module connects to external systems through a distributed data acquisition framework to collect and integrate multi-source heterogeneous data, including but not limited to medical text data, multimodal medical imaging data and real-time medical sensor data.

[0008] Intelligent health record analysis module: performs semantic segmentation, entity recognition and relationship extraction on medical text data to obtain key information, and structures the key information to obtain key medical information. The key medical information includes the type of disease, personal basic information, family genetic history, previous medical history, medication status and treatment plan.

[0009] The multimodal disease risk assessment module obtains key medical information from the intelligent health record analysis module, combines medical imaging and real-time medical sensor data to build a multimodal disease risk stratification model, and generates accurate risk stratification assessment results.

[0010] The potential disease prediction module is based on the time series knowledge graph and dynamic graph neural network, combined with the medical ontology knowledge base to build a spatiotemporal fusion analysis model to predict potential disease risks and complication risks.

[0011] According to one aspect of an embodiment of the present application, the system includes the following modules: an expert-level medical question-and-answer assistance module, which is based on domain-adaptive large language model technology, performs domain-oriented fine-tuning on multi-source heterogeneous data reviewed by experts through medical knowledge distillation and comparative learning methods, and combines a multimodal context-aware dialogue management system to obtain precise medical consultation and personalized health intervention recommendations.

[0012] According to one aspect of an embodiment of the present application, the external system includes a public health management system, a hospital information system, a family doctor contract system, a regional health information platform, and a wearable device health monitoring system.

[0013] According to one aspect of an embodiment of the present application, the intelligent health record analysis module includes a medical text enhancement preprocessing engine, a multi-level text classification system, and a deep information extraction and structuring framework.

[0014] The medical text enhancement preprocessing engine preprocesses medical text data through medical stem extraction, medical abbreviation expansion, and context-dependent ambiguity elimination; and applies medical ontology mapping to standardize medical terms to achieve a unified text representation.

[0015] A multi-level text classification system, based on a hierarchical attention network, accurately classifies medical text data into a predefined multi-level category system; the category system includes symptom type, disease severity, diagnosis category, medication status and treatment plan category.

[0016] The deep information extraction and structuring framework uses medical named entity recognition and relationship extraction technology, combined with medical knowledge graph constraints, to convert the extracted key information into structured key medical information.

[0017] According to one aspect of an embodiment of the present application, a multimodal disease risk assessment module.

[0018] Based on key medical information, multimodal medical imaging data and real-time medical sensor data are adaptively selected through feature importance analysis; a modality-specific feature extraction network is used to extract modal features of different modal data, and an interactive multi-head self-attention mechanism is used to dynamically fuse the weights of different modal features to obtain cross-modal fusion features to capture the complementarity and synergy between different modalities.

[0019] Adversarial autoencoders are applied to denoise and compress cross-modal fusion features to improve the robustness of feature representation and reduce redundant features.

[0020] Nonlinear manifold learning technology is combined to perform nonlinear dimensionality reduction on high-dimensional cross-modal fusion features to retain key diagnostic features; relevant domain knowledge is transferred from the pre-trained model through transfer learning methods to enhance the pre-trained model's ability to recognize scarce cases.

[0021] Based on an integrated learning framework, a multimodal disease risk stratification model is constructed by integrating gradient boosting decision trees, deep neural networks, and support vector machines to generate accurate risk stratification assessment results, including risk scores and interpretable risk factor analysis reports.

[0022] According to one aspect of an embodiment of the present application, features of data of different modalities are extracted: the modal features include personal information semantic features, dense vector representation of medical text, deep features of medical images, and time series sensor data features.

[0023] According to one aspect of an embodiment of the present application, the potential disease prediction module: uses a temporal graph neural network combined with a medical ontology knowledge base to construct a dynamic heterogeneous graph structure analysis model G = (V, E, A, R), where V is a node set representing a medical entity, E is an edge set representing the relationship between medical entities, A is a node attribute set, and R is an edge relationship type set.

[0024] According to one aspect of an embodiment of the present application, the dynamic heterogeneous graph structure analysis model adopts a heterogeneous graph attention convolution layer, so that each node updates its own representation by selectively aggregating the information of its neighboring nodes, and the graph pooling layer adopts a differentiable pooling method to capture multi-scale graph structure information; the dynamic heterogeneous graph structure analysis model integrates a temporal evolution component to capture the temporal dynamic characteristics of the development of the disease and predict the disease progression trajectory.

[0025] According to one aspect of an embodiment of the present application, a dynamic heterogeneous graph structure analysis model.

[0026] Medical entities include disease types, diagnostic items, treatment plans and medication conditions.

[0027] Node attributes include the patient's key medical information and statistical characteristics; the node hidden feature representation is extracted from the node attributes through a graph autoencoder, and different types of node features are adaptively fused using intra-graph and inter-graph multi-head self-attention mechanisms to obtain context-aware fused node features, thereby enhancing the semantic richness of the node representation.

[0028] According to one aspect of an embodiment of the present application, a dynamic heterogeneous graph structure analysis model.

[0029] The edge weights are dynamically calculated through the edge attention mechanism constrained by medical knowledge to represent the association strength and confidence between medical entities. The association strength and confidence between medical entities are jointly calculated through medical knowledge and statistical correlation to construct a probabilistic graph structure analysis model with uncertainty assessment capabilities.

[0030] The probabilistic graph structure analysis model uses a graph reasoning algorithm optimized by reinforcement learning to assess the risk of potential diseases and predict complications.

[0031] Compared with the prior art, this application has the following beneficial effects.

[0032] 1. To address this problem, the multi-source heterogeneous data intelligent acquisition module of this application has designed an intelligent data interface adapter that supports multiple medical data transmission protocols such as RESTAPI, HL7FHIR, DICOM, and realizes data standardization mapping through an efficient data conversion engine, solving the problem of data interoperability across institutions and systems within the medical community.

[0033] 2. The intelligent health record analysis module of this application combines deep learning and traditional natural language processing technologies to build a hybrid model. Through the medical text enhancement preprocessing engine, multi-level text classification system and deep information extraction and structuring framework, it realizes the automated processing of unstructured medical text data, and converts medical information scattered in various medical documents into standardized structured data, greatly improving the utilization value of medical data.

[0034] 3. This application introduces a dynamic heterogeneous graph structure analysis model G = (V, E, A, R), and uses a temporal graph neural network combined with a medical ontology knowledge base to represent medical entities such as disease types, diagnostic items, treatment plans, and drug types and their relationships as a dynamic graph structure. It also integrates medical data features of different modalities through a multi-head self-attention mechanism within and between graphs. At the same time, the system integrates a temporal evolution component that can track and predict the development trajectory of the disease, realizing a shift from passive diagnosis and treatment to active prevention, and providing technical support for hierarchical diagnosis and treatment and precise health management within the medical community. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A structural diagram of the medical community platform management system provided for one embodiment of the present application. DETAILED DESCRIPTION

[0036] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Throughout the drawings, identical or similar reference numerals represent identical or similar elements or elements having identical or similar functions. The described embodiments are only some, not all, of the embodiments of the present invention.

[0037] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0038] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0039] Please refer to Figure 1 The medical community platform management system provided in one embodiment of the present application includes the following modules.

[0040] The multi-source heterogeneous data intelligent acquisition module connects to external systems via a distributed data acquisition framework. This distributed data acquisition framework distributes acquisition tasks across multiple servers and devices, enabling efficient data acquisition from large-scale, distributed data sources. It collects and integrates multi-source heterogeneous data, including but not limited to medical text data, multimodal medical imaging data, and real-time medical sensor data. Medical text data includes textual materials such as medical records and diagnostic reports; multimodal medical imaging data includes medical images generated by various imaging technologies, such as CT, MRI, and X-ray; and real-time medical sensor data includes physiological parameters collected in real time by wearable devices, bedside monitors, and other devices.

[0041] Intelligent health record analysis module: performs semantic segmentation, entity recognition and relationship extraction on medical text data to obtain key information, and structures the key information to obtain key medical information. The key medical information includes the type of disease, personal basic information, family genetic history, previous medical history, medication status and treatment plan.

[0042] Semantic segmentation is a technology that divides medical text into semantic units based on its semantic content. Entity recognition automatically identifies medical concept entities (such as disease names, drug names, symptoms, etc.) from text. Relation extraction identifies semantic relationships between entities in text (such as "Drug A treats Disease B"). Structuring converts unstructured text into a data structure with clear formats and relationships.

[0043] The multimodal disease risk assessment module obtains key medical information from the intelligent health record analysis module, combines medical imaging and real-time medical sensor data to build a multimodal disease risk stratification model, and generates accurate risk stratification assessment results.

[0044] The potential disease prediction module is based on the time series knowledge graph and dynamic graph neural network, combined with the medical ontology knowledge base to build a spatiotemporal fusion analysis model to predict potential disease risks and complication risks.

[0045] As an optional solution of this application, the expert-level medical question-answering auxiliary module is based on domain-adaptive large language model technology. Through medical knowledge distillation and comparative learning methods, it performs domain-specific fine-tuning on multi-source heterogeneous data reviewed by experts, and combines it with a multimodal context-aware dialogue management system to obtain precise medical consultation and personalized health intervention recommendations.

[0046] As an optional solution of this application, the external system includes a public health management system, a hospital information system, a family doctor contract system, a regional health information platform and a wearable device health monitoring system.

[0047] The intelligent acquisition module for source heterogeneous data includes the following components: an intelligent data interface adapter, which is used for adaptive connection with interfaces of different external systems, supports multiple medical data transmission protocols and formats such as RESTAPI, HL7FHIR, DICOM, and has the ability to automatically identify and dynamically match protocols.

[0048] The intelligent data interface adapter implements a dynamic service discovery mechanism in the intelligent data interface adapter, automatically defines standard data interfaces for connecting to various external systems; configures adaptive connection settings, which include multi-factor authentication information, interface address mapping table, data request parameter template and exception retry strategy; the intelligent data interface adapter has the following features: interface version compatibility management, supporting forward compatibility and backward compatibility of different versions of medical information system interfaces; dynamic load balancing capability, automatically adjusting data collection task allocation according to system load and priority; incremental synchronization mechanism, synchronizing only changed data through change data capture technology to reduce system load.

[0049] An efficient data conversion engine is responsible for normalizing heterogeneous data obtained from external systems into a unified standard format, realizing data standardization mapping, and ensuring data consistency, integrity, and interoperability.

[0050] A hierarchical data storage architecture uses hybrid storage technology to store and manage heterogeneous data collected from external systems, including relational databases for storing structured data, NoSQL databases for storing semi-structured data, and distributed file systems for storing unstructured data.

[0051] The data quality monitoring component performs real-time quality monitoring of collected data through data quality assessment algorithms, and automatically identifies and processes missing values, outliers, and inconsistent data.

[0052] As an optional solution of this application, the intelligent health record analysis module includes a medical text enhancement preprocessing engine, a multi-level text classification system and a deep information extraction and structuring framework.

[0053] The medical text enhancement preprocessing engine preprocesses medical text data through medical stem extraction, medical abbreviation expansion and context-dependent ambiguity resolution; medical ontology mapping is applied to standardize medical terms and use unified text representation; medical stem extraction is to extract the roots of medical terms; medical abbreviation expansion is to convert medical abbreviations into full terms; context-dependent ambiguity resolution is to resolve term ambiguity based on the context.

[0054] A multi-level text classification system accurately classifies medical text data into a predefined multi-level category system based on a hierarchical attention network. The hierarchical attention network is a deep learning model that can assign different attention weights at multiple levels for text classification. The category system includes disease type, disease severity, diagnosis category, medication use, and treatment plan category.

[0055] The deep information extraction and structuring framework uses medical named entity recognition and relationship extraction technology, combined with medical knowledge graph constraints, to convert the extracted key information into structured key medical information.

[0056] Key medical information: This is often scattered in unstructured text and may be inconsistently expressed and formatted. For example, a description in a medical record reads: "The patient reported that he began to experience intermittent headaches three days ago, accompanied by mild nausea and no fever. He has a history of hypertension and is currently taking valsartan tablets to control it."

[0057] Structured data format: information organized according to a predefined data model or framework; with clear field names, data types, and value ranges; convenient for database storage and computer analysis; for example, after the above information is structured:; {"Symptoms":[{"Name":"Headache","Nature":"Intermittent","Start Time":"Three days ago"},{"Name":"Nausea","Degree":"Mild","Status":"Accompanied"}],"Exclusion symptoms":["Fever"],"Past history":[{"Disease":"Hypertension"}],"Current medication":[{"Drug name":"Valsartan tablets","Purpose":"Control blood pressure"}]}.

[0058] This conversion process usually involves: medical entity recognition - identifying medical terms such as diseases, symptoms, and drugs in the text; relationship extraction - determining the relationship between entities (such as "taking medication", "suffering from a disease"); temporal information extraction - determining the time point and duration of medical events; standardized mapping - mapping medical terms to standard medical vocabulary (such as ICD-10, SNOMEDCT); structured representation generation - organizing information into a predefined data structure; through this conversion, medical text information that was originally difficult to use directly becomes structured data that can be processed by computers, providing a basis for subsequent analysis and decision-making.

[0059] As an optional solution of this application, a multimodal disease risk assessment module is provided. Based on key medical information, multimodal medical imaging data and real-time medical sensor data are adaptively selected through feature importance analysis. A modality-specific feature extraction network is used to extract modal features of data from different modalities. An interactive multi-head self-attention mechanism is used to dynamically weight the features of different modalities to obtain cross-modal fusion features to capture the complementarity and synergy between different modalities. Feature importance analysis is used to evaluate the contribution of different features to model prediction and select the most valuable features. The modality-specific feature extraction network is designed for different data modalities (such as text, images, and time series data). The interactive multi-head self-attention mechanism is an attention mechanism that can simultaneously focus on different representation subspaces and is used to fuse multimodal features.

[0060] Adversarial autoencoders are applied to denoise and compress cross-modal fusion features to improve the robustness of feature representation and reduce redundant features.

[0061] Nonlinear manifold learning technology is combined to perform nonlinear dimensionality reduction on high-dimensional cross-modal fusion features to retain key diagnostic features; relevant domain knowledge is transferred from the pre-trained model through transfer learning methods to enhance the pre-trained model's ability to recognize scarce cases.

[0062] Based on an integrated learning framework, a multimodal disease risk stratification model is constructed by integrating gradient boosting decision trees, deep neural networks, and support vector machines to generate accurate risk stratification assessment results, including risk scores and interpretable risk factor analysis reports.

[0063] Modality-specific features are unique features extracted from data of different modalities. In healthcare systems, this concept refers to using feature extraction methods designed specifically for each type of medical data (such as text, images, and sensor data) to obtain information characteristics unique to that modality.

[0064] Medical data from different modalities has different data structures and characteristics: medical text data has semantic and contextual features, medical images have spatial, textural, and morphological features, and medical sensor data has temporal and frequency domain features. A "modality-specific feature extraction network" is a specialized feature extractor designed for each data modality: A natural language processing model is used to extract semantic features from medical text; a convolutional neural network is used to extract visual features from medical images; and a timing model is used to extract time series features from sensor data. In a multimodal medical system, this approach preserves the unique information of each data type and then integrates the features of different modalities through fusion technology to form a more comprehensive representation of symptoms.

[0065] A multi-head self-attention mechanism is used to fuse features from different modalities to generate fused features that capture the interrelationships between them: Attention(Q,K,V) = softmax(QKT / dk1 / 2)V, where Q, K, and V are the feature matrices of the different modalities, and dk is a scaling factor. An autoencoder is used to compress the fused features to reduce redundant features: z = σ(Weh + be), where h is the fused feature, z is the encoded feature representation, and We and be are the encoder weights and bias terms. The high-dimensional features are further reduced using the PCA dimensionality reduction algorithm to retain the key features: Xreduced = XW, where X is the input feature matrix and W is the principal component matrix. This results in a multimodal disease analysis model. Using this multimodal disease analysis model, risk prediction is performed using a fully connected layer or multi-layer perceptron to obtain a disease risk score: P(y|x) = softmax(Woutz + bout), where Wout and bout are the weights and bias terms of the output layer.

[0066] When training, the multimodal disease risk stratification model needs to be based on existing medical scoring standards, such as the SOFA score, CHA2DS2-VASc score, etc., combined with the expert knowledge base to label the existing data. The disease risk prediction task can be regarded as a regression problem. The goal is to predict a continuous risk score. The loss function uses the mean square error, LMSE = 1 / N∑i = 1N(yi-y^i)2, where N is the number of samples, yi is the actual risk score of sample i, and y^i is the risk score predicted by the multimodal disease analysis model.

[0067] As an optional solution for this application, modal features include personal information semantic features, dense vector representations of medical text, deep medical image features, and time series sensor data features. Personal information semantic features: semantic features extracted from basic personal information; dense vector representations of medical text: high-dimensional vectors of text; deep medical image features: features extracted from deep learning of medical images; and time series sensor data features: time series features extracted from sensor data.

[0068] A fully connected neural network is used to extract personal information features for personal information; the personal information includes height, weight, age and gender.

[0069] For structured text data such as past medical history, current diagnosis, and medication status, the word embedding model Word2Vec is used to convert the text part into a dense vector representation. w =Word2Vec(w), where w is a word and v is a w It is a dense vector representation. Specifically, words are mapped into a dense vector space. Words with similar semantics are closer in the vector space. Pre-trained Word2Vec can be fine-tuned based on text data in specific medical fields to obtain more accurate representations. Averaging all word vectors or using LSTM to model text sequences generates an overall representation of the text. text =1 / N∑ i＝1 N v wi or v text =LSTM(v w1 ,v w2 ,…,v wN ). For medical images, ResNet convolutional neural network is used to extract medical image features. image =ResNet(Image), the feature extraction formula of each convolutional layer of ResNet is, l+1 =x l +F(x l ,W l ), where F(x l ,W l ) is the convolution operation.

[0070] As an optional solution of this application, the potential disease prediction module: uses a temporal graph neural network combined with a medical ontology knowledge base to construct a dynamic heterogeneous graph structure analysis model G = (V, E, A, R), where V is a node set, representing medical entities, such as disease types, diagnostic items, treatment plans and medication conditions, etc. Each node has its own specific attributes and characteristics, E is an edge set, representing the relationship between medical entities, such as the association between symptoms and diagnostic items, the correspondence between diagnostic items and treatment plans, etc. Each edge also has its corresponding attributes and weights, indicating the strength and nature of the relationship; A is a node attribute set, such as the patient's key medical information (symptoms, medical history, etc.), statistical characteristics (age, gender distribution, etc.), etc. These attributes provide rich semantic information for the nodes, which helps to more accurately analyze and understand medical entities; R is an edge relationship type set; different edge relationship types are defined, such as causal relationships, association relationships, inclusion relationships, etc. By distinguishing different edge relationship types, the complex relationships between medical entities can be more carefully portrayed, providing a more accurate basis for the analysis and reasoning of graph models.

[0071] As an optional solution of this application; the dynamic heterogeneous graph structure analysis model adopts a heterogeneous graph attention convolution layer, so that each node updates its own representation by selectively aggregating the information of its neighboring nodes. This can highlight the influence of important neighboring nodes and ignore the interference of unimportant nodes, thereby more effectively capturing the information contained in different node types and relationships in the heterogeneous graph, and improving the model's ability to learn node representations.

[0072] The graph pooling layer uses a differentiable pooling method to capture multi-scale graph structural information; it performs hierarchical pooling operations in a differentiable manner to capture multi-scale graph structural information; it can gradually reduce the scale and complexity of the graph while retaining the main structural features of the graph, and extract graph representations at different levels, which helps to improve the computational efficiency of the graph model and its ability to process large-scale graph data; the dynamic heterogeneous graph structure analysis model integrates time-series evolution components to capture the temporal dynamic characteristics of disease development and predict the disease progression trajectory.

[0073] As an optional solution of this application; a dynamic heterogeneous graph structure analysis model.

[0074] Medical entities include disease types, diagnostic items, treatment plans and medication conditions.

[0075] Node attributes include the patient's key medical information and statistical characteristics; the node hidden feature representation is extracted from the node attributes through a graph autoencoder, the graph structure and node features are mapped to a low-dimensional space through the encoder, and then the graph structure and node attribute information are reconstructed through the decoder, thereby learning the node hidden feature representation that can effectively retain the structure and feature information in the graph.

[0076] The multi-head self-attention mechanism within and between graphs is used to adaptively fuse different types of node features to obtain context-aware fused node features; the semantic richness of node representation is enhanced; not only the attention weights between nodes are calculated within a single graph, but also the relationships and interactions between different graphs are considered. Under the framework of the multi-head self-attention mechanism, different types of node features can be adaptively fused from multiple different angles and granularities to obtain context-aware fused node features, which enhances the semantic richness of node representation and improves the model's ability to understand and analyze complex graph structure data.

[0077] As an optional solution of this application; dynamic heterogeneous graph structure analysis model: the edge weights are dynamically calculated through the edge attention mechanism constrained by medical knowledge to represent the association strength and confidence between medical entities. The edge weights can be dynamically adjusted according to different tasks and contexts, so that the model can more accurately capture the association relationship between medical entities.

[0078] By jointly calculating medical knowledge and statistical correlation, the association strength and confidence between medical entities are represented, and a probabilistic graph structure analysis model with uncertainty assessment capabilities is constructed.

[0079] The probabilistic graph structure analysis model uses a graph reasoning algorithm optimized by reinforcement learning to perform potential disease risk assessment and complication prediction. The probabilistic graph structure analysis model can model and analyze the uncertainty and randomness in the graph by assigning probability distributions to the nodes and edges in the graph. In this patent, the probabilistic graph structure analysis model uses an edge attention mechanism to dynamically calculate edge weights to construct a probabilistic graph structure analysis model with uncertainty assessment capabilities. It can more comprehensively reflect the associations and uncertainties between medical entities and provide more reliable results for potential disease risk assessment and complication prediction. Through the reward mechanism and strategy updates in reinforcement learning, the algorithm can automatically learn better reasoning paths and strategies, improve the efficiency and accuracy of graph reasoning, and thus more effectively perform potential disease risk assessment and complication prediction.

[0080] This application introduces a dynamic heterogeneous graph structure analysis model G = (V, E, A, R), uses a temporal graph neural network combined with a medical ontology knowledge base, and represents medical entities such as disease types, diagnostic items, treatment plans, and drug types and their relationships as a dynamic graph structure. It also integrates medical data features of different modalities through a multi-head self-attention mechanism within and between graphs. At the same time, the system integrates a temporal evolution component that can track and predict the development trajectory of the disease, achieving a shift from passive diagnosis and treatment to active prevention, and providing technical support for hierarchical diagnosis and treatment and precise health management within the medical community.

Claims

1. A medical community platform management system, characterized in that: The system includes the following modules: A multi-source heterogeneous data intelligent acquisition module, which connects to external systems through a distributed data acquisition framework to collect and integrate multi-source heterogeneous data, including but not limited to medical text data, multimodal medical imaging data, and real-time medical sensor data; Intelligent health record analysis module: performs semantic segmentation, entity recognition, and relationship extraction on medical text data to obtain key information, and structures the key information to obtain key medical information. The key medical information includes the type of disease, basic personal information, family genetic history, previous medical history, medication status, and treatment plan; The multimodal disease risk assessment module obtains key medical information from the intelligent health record analysis module, combines medical imaging and real-time medical sensor data to build a multimodal disease risk stratification model, and generates accurate risk stratification assessment results; The potential disease prediction module is based on the time series knowledge graph and dynamic graph neural network, combined with the medical ontology knowledge base to build a spatiotemporal fusion analysis model to predict potential disease risks and complication risks.

2. The medical community platform management system according to claim 1 is characterized in that: The system includes the following modules: The expert-level medical question-answering assistance module is based on domain-adaptive large language model technology. Through medical knowledge distillation and comparative learning methods, it performs domain-specific fine-tuning on multi-source heterogeneous data reviewed by experts. Combined with a multimodal context-aware dialogue management system, it obtains precise medical consultation and personalized health intervention recommendations.

3. The medical community platform management system according to claim 1 is characterized in that: External systems include public health management systems, hospital information systems, family doctor contract systems, regional health information platforms, and wearable device health monitoring systems.

4. The medical community platform management system according to claim 1 is characterized in that: The intelligent health record analysis module includes a medical text enhancement preprocessing engine, a multi-level text classification system, and a deep information extraction and structuring framework; Medical text enhancement preprocessing engine, which preprocesses medical text data through medical stemming, medical abbreviation expansion, and context-dependent ambiguity elimination; and applies medical ontology mapping to standardize medical terminology to achieve a unified text representation; A multi-level text classification system, based on a hierarchical attention network, accurately classifies medical text data into a predefined multi-level classification system; the classification system includes disease type, disease severity, diagnosis category, medication status, and treatment plan category; The deep information extraction and structuring framework uses medical named entity recognition and relationship extraction technology, combined with medical knowledge graph constraints, to convert the extracted key information into structured key medical information.

5. The medical community platform management system according to claim 4 is characterized in that: Multimodal disease risk assessment module; Based on key medical information, multimodal medical imaging data and real-time medical sensor data are adaptively selected through feature importance analysis. Modal features of different modal data are extracted using a modality-specific feature extraction network. Dynamic weighted fusion of different modal features is performed using an interactive multi-head self-attention mechanism to obtain cross-modal fusion features to capture the complementarity and synergy between different modalities. Apply adversarial autoencoders to denoise and compress cross-modal fusion features to improve the robustness of feature representation and reduce redundant features; Combining nonlinear manifold learning technology to perform nonlinear dimensionality reduction on high-dimensional cross-modal fusion features, retaining key diagnostic features; Transferring relevant domain knowledge from pre-trained models through transfer learning methods can enhance the pre-trained models’ ability to identify scarce cases. Based on an integrated learning framework, a multimodal disease risk stratification model is constructed by integrating gradient boosting decision trees, deep neural networks, and support vector machines to generate accurate risk stratification assessment results, including risk scores and interpretable risk factor analysis reports.

6. The medical community platform management system according to claim 5 is characterized in that: Extract features of data from different modalities: The modal features include personal information semantic features, dense vector representation of medical text, deep features of medical images, and time series sensor data features.

7. The medical community platform management system according to claim 1 is characterized in that: Potential disease prediction module: Use the time-series graph neural network combined with the medical ontology knowledge base to construct a dynamic heterogeneous graph structure analysis model G=, where V is a node set representing medical entities, E is an edge set representing the relationship between medical entities, A is a node attribute set, and R is an edge relationship type set.

8. The medical community platform management system according to claim 7 is characterized in that: The dynamic heterogeneous graph structure analysis model uses a heterogeneous graph attention convolution layer to enable each node to update its own representation by selectively aggregating information from its neighboring nodes. The graph pooling layer uses a differentiable pooling method to capture multi-scale graph structure information. The dynamic heterogeneous graph structure analysis model integrates a temporal evolution component to capture the temporal dynamic characteristics of disease development and predict the disease progression trajectory.

9. The medical community platform management system according to claim 8 is characterized in that: Dynamic heterogeneous graph structure analysis model: Medical entities include disease types, diagnostic items, treatment plans, and medications; Node attributes include key medical information and statistical features of patients. A graph autoencoder is used to extract node hidden feature representations from these attributes. A multi-head self-attention mechanism within and between graphs is used to adaptively fuse different types of node features to obtain context-aware fused node features. Enhance the semantic richness of node representation.

10. The medical community platform management system according to claim 9 is characterized in that: Dynamic heterogeneous graph structure analysis model: The edge weights are dynamically calculated through the edge attention mechanism constrained by medical knowledge to represent the strength and confidence of the association between medical entities. The strength and confidence of the association between medical entities are jointly calculated through medical knowledge and statistical correlation to construct a probabilistic graph structure analysis model with uncertainty assessment capabilities. The probabilistic graph structure analysis model uses a graph reasoning algorithm optimized by reinforcement learning to assess the risk of potential diseases and predict complications.

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