BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.

Journal: Frontiers in public health
PMID: 38716250

Abstract

INTRODUCTION: Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain.

Authors

  • Fan Dong
    National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA.
  • Wenjing Guo
    National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States.
  • Jie Liu
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Tucker A Patterson
    National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States.
  • Huixiao Hong
    National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA. Electronic address: Huixiao.Hong@fda.hhs.gov.

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