DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Sep 18, 2021

Abstract

OBJECTIVE: Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identifying the span of ADE mentions, and ADE mention normalization to standardized terminologies. While the common goal of such systems is to detect ADE signals that can be used to inform public policy, it has been impeded largely by limited end-to-end solutions for large-scale analysis of social media reports for different drugs.

Authors

  • Arjun Magge
    Health Language Processing Center, Institute for Biomedical Informatics at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Elena Tutubalina
    Kazan (Volga Region) Federal University, Kazan, Russia.
  • Zulfat Miftahutdinov
    Kazan Federal University, 18 Kremlyovskaya street, Kazan 420008, Russian Federation. Electronic address: zulfatmi@gmail.com.
  • Ilseyar Alimova
    Kazan Federal University, 18 Kremlyovskaya Street, Kazan 420008, Russian Federation. Electronic address: alimovailseyar@gmail.com.
  • Anne Dirkson
    LIACS, Leiden University, Leiden, Netherlands.
  • Suzan Verberne
    Leiden Institute for Advanced Computer Sciences, Leiden University, Niels Bohrweg 1, 2333, CA, Leiden, The Netherlands.
  • Davy Weissenbacher
    Health Language Processing Center, Institute for Biomedical Informatics at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Graciela Gonzalez-Hernandez
    Health Language Processing Center, Institute for Biomedical Informatics at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

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