基于BERT和领域词典融合的中文电子病历命名实体识别  

Named Entity Recognition of Chinese Electronic Medical Record Based on BERT and Domain Dictionary

作　　者：叶恩光 张晓如[1] 张再跃[1] 丁腊春 朱向南 王译 YE Enguang;ZHANG Xiaoru;ZHANG Zaiyue;DING Lachun;ZHU Xiangnan;WANG Yi(Jiangsu University of Science and Technology,Zhenjiang 212003;The Fourth People's Hospital of Zhenjiang,Zhenjiang 212001)

机构地区：[1]江苏科技大学,镇江212003 [2]镇江市第四人民医院,镇江212001

出　　处：《计算机与数字工程》2024年第3期746-750,767,共6页Computer & Digital Engineering

基　　金：江苏省重大科技示范项目(编号:BE2018700);国家自然科学基金项目(编号:61371114)资助。

摘　　要：医疗数据挖掘的起始环节为CNER(中文电子病历命名实体识别),将相关实体(解剖部位、药品、影像检查等)识别出非结构化文本是其目标所在。基于CNER准确性提升需要,论文设计了BERT-BiLSTM-CRF模型融合领域词典技术,该技术能将上下文语义关系全面结合,一词多义问题同样可以迎刃而解,获取电子病历句子的长距离依赖。CNER采用BERT-BiLSTM-CRF模型融合领域词典技术时的F1值已经被实验结果所证实,对知识图谱的构建、临床决策支持系统和病历质控系统等的研究有着重要意义。The beginning of medical data mining is CNER(named entity recognition of Chinese electronic medical record).The target of medical data mining is to recognize unstructured text from related entities(anatomical parts,drugs,image examina-tion,etc.).Based on the need of improving the accuracy of CNER,This paper designs the BERT-BiLSTM-CRF model fusion do-main dictionary technology,which can fully combine the context semantic relationship,solve the polysemy problem,and obtain the long-distance dependence of EMR sentences.When CNER uses the BERT-BiLSTM-CRF model to fuse the domain dictionary tech-nology,the value of F1 has been confirmed by the experimental results,which is of great significance to the construction knowledge graph,clinical decision support system and medical record quality control system.

关 键 词：中文电子病历 命名实体识别 BERT-BiLSTM-CRF 领域词典 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]