Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 33647938

Abstract

OBJECTIVE: Automated analysis of vaccine postmarketing surveillance narrative reports is important to understand the progression of rare but severe vaccine adverse events (AEs). This study implemented and evaluated state-of-the-art deep learning algorithms for named entity recognition to extract nervous system disorder-related events from vaccine safety reports.

Authors

  • Jingcheng Du
    University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Yang Xiang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Madhuri Sankaranarayanapillai
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Meng Zhang
    College of Software, Beihang University, Beijing, China.
  • Jingqi Wang
    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.
  • Huy Anh Pham
    Melax Technologies, Inc, Houston, Texas, USA.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yong Chen
    Department of Urology, Chongqing University Fuling Hospital, Chongqing, China.
  • Cui Tao
    The University of Texas Health Science Center at Houston, USA.

Keywords

External Resources

Popular Topics
Recent Journals