期刊论文

中文

计算机与现代化

1006-2475

2024

01

87-91

10.3969/j.issn.1006-2475.2024.01.014

2017YFC1703300:科技部十三五重点研发计划 2030;2018AAA0102102:科技创新新一代人工智能重大项目

本校为第一机构

电子病历是保存、管理、传输病人医疗记录的重要资源,是医生诊治疾病的重要文本记录.通过电子病历命名实体识别(NER)技术能够高效、智能地从电子病历中抽取症状、疾病、药名等诊疗信息,有利于结构化电子病历,使之能够使用机器学习等技术进行诊疗规律挖掘.为了高效识别电子病历中的命名实体,提出一种融合对抗训练(FGM)的基于BERT与双向长短期记忆网络(BILSTM)的命名实体识别方法(BERT-BILSTM-CRF-FGM,BBCF),对2017全国知识图谱与语义计算大会(CCKS2017)提供的中文电子病历语料做修正等预处理后,采用BBCF模型识别该语料中5种实体的平均F1值为92.84%,比基于膨胀卷积网络的BERT模型(BERT-IDCNN-CRF)...

Electronic medical record is an important resource for the preservation,management and transmission of patients'medical records.It is also an important text record for doctors'diagnosis and treatment of diseases.Through the electronic medi-cal record named entity recognition(NER)technology,diagnosis and treatment information such as symptoms,diseases and drug names can be extracted from the electronic medical record efficiently and intelligently.It is helpful for structured electronic medical records to use machine learning and other technologies for diagnosis and treatment regularity mining.I...