中国医疗设备2025，Vol.40Issue(6)：44-48,5.DOI:10.3969/j.issn.1674-1633.20241314
基于电子病历和公开医学知识图谱的专病知识图谱构建
Construction of Specific Disease Knowledge Graph Based on Electronic Medical Records and Public Medical Knowledge Graph
摘要
Abstract
Objective To construct a clinical specific disease knowledge graph of stroke based on the public medical knowledge graph and electronic medical records.Methods The biomedical information ontology system and the structured electronic medical records of stroke patients were used as knowledge sources,the basic terminology list of stroke,the characteristic word list of patients,the concept dictionary of stroke,the triplet set of stroke relationships and the concept terminology set of stroke were constructed successively.The relational triplet set and the conceptual term set were imported into the Neo4j database to complete the construction of the knowledge graph of stroke specific diseases.The knowledge graph representation was obtained through the knowledge graph embedding model.Experiments were designed to take link prediction and triple classification as evaluation tasks to compare the performance differences of the graph embeddings obtained by the graph through models such as TransE,Rotate,and Analogy.In addition,two prediction tasks were designed for whether the patient's hospitalization exceeded 7 and 14 d.The embedding of the knowledge graph with the optimal performance was fused with the original feature patient representation based on the Skip-gram algorithm to construct a machine learning model to complete the prediction task and evaluate its performance.F1 score,area under curve(AUC)of receiver operating characteristic(ROC),and AUC of precision-recall rate were adopted as the evaluation indicators.Results The constructed knowledge graph for stroke specific diseases had 215090 entities and 550976 relationships,and the optimal graph embedding was obtained based on the RotatE model.The experimental results showed that,compared to the P-vector,the KGP-vector achieved improvements in the tasks of predicting whether a patient's hospitalization exceeds 7 and 14 d.Specifically,the F1 score,ROC AUC,and precision-recall AUC increased by 0.039,0.061,0.047 and 0.089,0.081,0.103,respectively.Conclusion By using the public medical knowledge graph combined with patient data,a high-quality specific disease knowledge graph can be rapidly constructed,which is expected to provide support for clinical decision-making,disease diagnosis and personalized medical treatment of stroke diseases.关键词
知识图谱/电子病历/脑卒中/生物医学信息本体系统/患者表示/预测模型Key words
knowledge graph/electronic medical record/stroke/biomedical information ontology system/patient stated/predictive model分类
预防医学引用本文复制引用
谢忠壤,王牧雨,范世玉,李一晨,陈卉..基于电子病历和公开医学知识图谱的专病知识图谱构建[J].中国医疗设备,2025,40(6):44-48,5.基金项目
国家自然科学基金项目(82372094) （82372094）
北京市自然科学基金(7252278). （7252278）
