A study of deep learning approaches for medication and adverse drug event extraction from clinical text.

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

Abstract

OBJECTIVE: This article presents our approaches to extraction of medications and associated adverse drug events (ADEs) from clinical documents, which is the second track of the 2018 National NLP Clinical Challenges (n2c2) shared task.

Authors

  • Qiang Wei
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Zongcheng Ji
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Zhiheng Li
    College of Computer Science and Technology, Dalian University of Technology, Dalian, China.
  • Jingcheng Du
    University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Jingqi Wang
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Jun Xu
    Department of Nephrology, The Affiliated Baiyun Hospital of Guizhou Medical University, Guizhou, China.
  • Yang Xiang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Firat Tiryaki
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Stephen Wu
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Yaoyun Zhang
    Alibaba Damo Academy, 969 West Wen Yi Road, Yu Hang District, Hangzhou, Zhejiang, China.
  • Cui Tao
    The University of Texas Health Science Center at Houston, USA.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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