MultiADE: A Multi-domain benchmark for Adverse Drug Event extraction.

Journal: Journal of biomedical informatics
PMID: 39536999

Abstract

OBJECTIVE: Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been created, and shared tasks have been organised to facilitate active adverse event surveillance. However, most - if not all - datasets or shared tasks focus on extracting ADEs from a particular type of text. Domain generalisation - the ability of a machine learning model to perform well on new, unseen domains (text types) - is under-explored. Given the rapid advancements in natural language processing, one unanswered question is how far we are from having a single ADE extraction model that is effective on various types of text, such as scientific literature and social media posts.

Authors

  • Xiang Dai
    CSIRO Data61, Sydney, Australia. Electronic address: dai.dai@csiro.au.
  • Sarvnaz Karimi
    Australian e-Health Research Centre, CSIRO, Royal Brisbane and Women's Hospital, Brisbane, Australia.
  • Abeed Sarker
    Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
  • Ben Hachey
    Annalise.ai, Sydney, New South Wales, Australia.
  • Cécile Paris
    Commonwealth Scientific and Industrial Research Organisation, Crn Vimiera and Pembroke Roads, Marsfield, NSW 2122, Australia.

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