Deep learning in clinical natural language processing: a methodical review.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Mar 1, 2020

Abstract

OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research.

Authors

  • Stephen Wu
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Kirk Roberts
    The University of Texas Health Science Center at Houston, USA.
  • Surabhi Datta
    IMO Health, Inc., Rosemont, IL 60018, United States.
  • Jingcheng Du
    University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Zongcheng Ji
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Yuqi Si
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Sarvesh Soni
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.
  • Qiong Wang
    Beijing Meiling Biotechnology Corporation, Beijing, 102600, PR China.
  • Qiang Wei
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Yang Xiang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Bo Zhao
    State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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