Adverse drug reaction detection via a multihop self-attention mechanism.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: The adverse reactions that are caused by drugs are potentially life-threatening problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their detrimental impacts on patients. Detecting ADRs through clinical trials takes a large number of experiments and a long period of time. With the growing amount of unstructured textual data, such as biomedical literature and electronic records, detecting ADRs in the available unstructured data has important implications for ADR research. Most of the neural network-based methods typically focus on the simple semantic information of sentence sequences; however, the relationship of the two entities depends on more complex semantic information.

Authors

  • Tongxuan Zhang
    College of Computer Science and Technology, Dalian University of Technology, Dalian, China.
  • Hongfei Lin
  • Yuqi Ren
    College of Computer Science and Technology, Dalian University of Technology, Dalian, China.
  • Liang Yang
  • Bo Xu
    State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
  • Zhihao Yang
    College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Yijia Zhang
    School of Computer Science and Technology, Dalian University of Technology, Dalian, China.