Medical Diagnosis Model Based On Knowledge Graph And Causal Reasoning

With the rapid development of information technology,intelligent medical diagnosis models are playing an increasingly important role in the medical field.By analyzing and processing massive amounts of medical data,these models extract medical knowledge from the data to help medical practitioners quickly understand patient information and make more accurate and reasonable medical decisions.Intelligent medical diagnosis models increase the utilization rate of medical resources,allowing limited resources to serve more people,which mitigates to some extent the problem of imbalanced distribution of medical resources and promotes development in the medical field.Therefore,the analysis and application of medical data using artificial intelligence technology to achieve medical intelligence has forward-looking research significance and high application value.As a semantic network that represents entity relationships,knowledge graphs can help medical models obtain information more accurately and quickly,effectively providing medical knowledge support and explanations for their predictive results.Causal reasoning can determine the causal path from disease features to disease causes based on the causal relationships in the knowledge graph,predict the type of disease the patient is suffering from,and discover the factors that lead to the occurrence of the disease.This helps medical practitioners to develop more accurate treatment plans,improve the quality and efficiency of medical care.The main purpose of this study is to conduct in-depth data mining on medical data and construct a more effective,fast,accurate,and sizable medical knowledge graph.Based on the knowledge graph,causal reasoning is performed to achieve disease prediction and etiology tracing.The main contents of this study include the following three aspects:(1)Construction of a medical knowledge graph: This study uses web crawler technology to collect data from popular medical knowledge websites in China,preprocesses the text medical data obtained as the data source for the medical knowledge graph.For the preprocessed unstructured medical data,this study uses a named entity recognition model based on BILSTM+CRF to identify medical named entities and proposes a relationship extraction model based on dependency relationships and graph neural networks to mine the relationships between medical entities and obtain structured medical relationship triple data.For the relationship extraction model proposed in this study,we conducted comparative experiments with three types of relationship extraction models on two public datasets,and the results showed that the F1 score of the relationship extraction model proposed in this study was 86.2%,which was superior to other comparative models,verifying the effectiveness of the proposed model.We use the py2 neo tool to connect to the Neo4 j database,import the structured medical relationship triple data into the Neo4 j database,and generate a visualized medical knowledge graph.(2)User intent analysis: This study proposes a user intent recognition model based on Bert+Text GAT.It treats user questions as text data,uses the text pre-training model Bert to capture the contextual information of the text,obtains the word embedding representation of the text,uses the co-occurrence matrix of words and syntactic dependency analysis to mine the structural information of the text,aggregates text information through the message passing mechanism of the graph attention network,recognizes user intent,and helps medical diagnosis models better understand patients’ needs and symptoms.(3)Disease prediction and etiology tracing: Combining the above two works,a medical diagnosis model is constructed.First,we construct a disease prediction model based on the knowledge graph and RCNN.Based on the user intent and disease symptom information contained in the user’s question,the corresponding disease is diagnosed.Then,a causality-based disease etiology tracing model is proposed to identify the factors that cause the disease,and help doctors formulate more accurate treatment plans to improve the quality and efficiency of medical treatment.