广西师范大学学报（自然科学版） ›› 2019, Vol. 37 ›› Issue (1): 80-88.doi: 10.16088/j.issn.1001-6600.2019.01.009

• 第二十四届全国信息检索学术会议专栏 • 上一篇    下一篇

基于循环胶囊网络的临床语义关系识别研究

王祺1, 邱家辉1, 阮彤1, 高大启1*, 高炬2   

  1. 1.华东理工大学信息科学与工程学院,上海200237;
    2.上海中医药大学附属曙光医院,上海200021
  • 收稿日期:2018-09-27 出版日期:2019-01-20 发布日期:2019-01-08
  • 通讯作者: 高大启(1957—),男,湖北宜昌人,华东理工大学教授,博士。E-mail: gaodaqi@ecust.edu.cn
  • 基金资助:
    国家自然科学基金(61772201);“精准医学研究”重大专项(2018YFC0910500);国家重大新药创制项目(2018ZX09201008)

Recurrent Capsule Network for Clinical Relation Extraction

WANG Qi1,QIU Jiahui1,RUAN Tong1,GAO Daqi1*,GAO Ju2   

  1. 1.School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;
    2.Shanghai Shuguang Hospital, Shanghai 200021, China
  • Received:2018-09-27 Online:2019-01-20 Published:2019-01-08

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摘要： 得益于医疗信息化的不断推进,医院已积累了大量的电子病历记录。然而,这些病历记录大多以自然语言的形式存在,无法为计算机所“理解”,也就无法对其做进一步的处理与挖掘。由此,对病历文本进行结构化研究,识别出病历实体间的语义关系,便显得尤为重要。本文针对临床语义关系识别任务,提出循环胶囊网络模型,使用分段循环神经网络来捕捉两实体及其上下文信息,并使用胶囊网络来进行最终的关系分类。实验表明,本文提出的方法较现有监督学习方法取得了更好的识别效果(F1-score为96.51%),证明了本文方法的优越性。

关键词: 电子病历记录, 关系识别, 循环神经网络, 胶囊网络, 深度学习

Abstract: A large number of electronic health records (EHRs) have been accumulated since the wide adoption of medical information systems in China. However, most of these records are written in natural language, which cannot be processed by computers directly. Thus, it is important to transform unstructured EHRs into structured ones. In this paper, a recurrent capsule network is proposed for clinical relation extraction in EHRs, where entity pairs and their contexts are captured by piece-wise recurrent neural network layers, and capsule layers are finally employed for relation classification. Experimental results show that this model performs better than the existing supervised methods, achieving a F1-score of 96.51%.

Key words: electronic health record, relation extraction, recurrent neural network, capsule network, deep learning

中图分类号: 

  • TP391
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