@inproceedings{durlich-etal-2025-explainability,
    title = "Explainability for {NLP} in Pharmacovigilance: A Study on Adverse Event Report Triage in {S}wedish",
    author = {D{\"u}rlich, Luise  and
      Bergman, Erik  and
      Larsson, Maria  and
      Dalianis, Hercules  and
      Doyle, Seamus  and
      Westman, Gabriel  and
      Nivre, Joakim},
    editor = "Ananiadou, Sophia  and
      Demner-Fushman, Dina  and
      Gupta, Deepak  and
      Thompson, Paul",
    booktitle = "Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.cl4health-1.5/",
    pages = "46--68",
    ISBN = "979-8-89176-238-1",
    abstract = "In fields like healthcare and pharmacovigilance, explainability has been raised as one way of approaching regulatory compliance with machine learning and automation.This paper explores two feature attribution methods to explain predictions of four different classifiers trained to assess the seriousness of adverse event reports. On a global level, differences between models and how well important features for serious predictions align with regulatory criteria for what constitutes serious adverse reactions are analysed. In addition, explanations of reports with incorrect predictions are manually explored to find systematic features explaining the misclassification.We find that while all models seemingly learn the importance of relevant concepts for adverse event report triage, the priority of these concepts varies from model to model and between explanation methods, and the analysis of misclassified reports indicates that reporting style may affect prediction outcomes."
}