现代情报2024，Vol.44Issue(9)：71-81,11.DOI:10.3969/j.issn.1008-0821.2024.09.006
基于异构图注意力网络的药物不良反应实体关系联合抽取研究
Joint Extraction of Adverse Drug Reactions Entities and Relations Based on Heterogeneous Graph Attention Network
摘要
Abstract
[Purpose/Significance]Joint extraction of entities and relations is a crucial component in the adverse drug reactions monitoring and knowledge organization.To address the issues of error propagation,entity redundancy and interac-tion deficiency in traditional pipeline extraction methods,and to improve the extraction effect of overlapping ternary groups of adverse drug reactions,the paper proposes a joint extraction model of adverse drug reactions entities and relations based on heterogeneous graph attention network MF-HGAT.[Method/Process]Firstly,the paper conducted knowledge trans-fered from external medical corpus resources through pre-training with BERT to achieve the fusion of multiple semantic fea-tures.Secondly,the paper introduced relations information as prior knowledge for heterogeneous graph nodes to avoid ex-tracting semantically irrelevant entities.Then,the paper enhanced the representations of characters and relations nodes by iteratively fusing messages with a hierarchical graph attention network through message passing.Finally,the paper extracted drug adverse reactions entities and relations after updating the node representations.[Result/Conclusion]Experiments on self-constructed adverse drug reactions datasets reveal that the joint extraction F1 value of MF-HGAT,which incorporates relations information and external medical and health domain knowledge,reaches 92.75%,which is an improvement of 5.29%over the mainstream model CasRel.The results demonstrate that the MF-HGAT model further enriches entity-rela-tions semantic information by fusing character and relations node semantics through heterogeneous graph attention network,which is of great significance to the knowledge discovery of adverse drug reactions.关键词
异构图注意力网络/实体关系联合抽取/药物不良反应/关系重叠/知识发现Key words
heterogeneous graph attention network/joint entity relation extraction/adverse drug reactions/relations overlap/knowledge discovery分类
社会科学引用本文复制引用
仲雨乐,韩普,许鑫..基于异构图注意力网络的药物不良反应实体关系联合抽取研究[J].现代情报,2024,44(9):71-81,11.基金项目
国家社会科学基金项目"面向多模态医疗健康数据的知识组织模式研究"(项目编号:22BTQ096) （项目编号:22BTQ096）
江苏高校青蓝工程和南京邮电大学1311人才计划. （）
