Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text.

Journal: BMC medical informatics and decision making
Published Date: Jan 31, 2019

Abstract

BACKGROUND: Extracting relations between important clinical entities is critical but very challenging for natural language processing (NLP) in the medical domain. Researchers have applied deep learning-based approaches to clinical relation extraction; but most of them consider sentence sequence only, without modeling syntactic structures. The aim of this study was to utilize a deep neural network to capture the syntactic features and further improve the performances of relation extraction in clinical notes.

Authors

  • Zhiheng Li
    College of Computer Science and Technology, Dalian University of Technology, Dalian, China.
  • Zhihao Yang
    College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
  • Chen Shen
    Department of Foreign Languages, Xi'an Jiaotong University City College, Xi'an, China.
  • Jun Xu
    Department of Nephrology, The Affiliated Baiyun Hospital of Guizhou Medical University, Guizhou, China.
  • Yaoyun Zhang
    Alibaba Damo Academy, 969 West Wen Yi Road, Yu Hang District, Hangzhou, Zhejiang, China.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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