@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Explainability for NLP in Pharmacovigilance: A Study on Adverse Event Report Triage in Swedish
%A Dürlich, Luise
%A Bergman, Erik
%A Larsson, Maria
%A Dalianis, Hercules
%A Doyle, Seamus
%A Westman, Gabriel
%A Nivre, Joakim
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%Y Gupta, Deepak
%Y Thompson, Paul
%S Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-238-1
%F durlich-etal-2025-explainability
%X 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.
%U https://aclanthology.org/2025.cl4health-1.5/
%P 46-68
Markdown (Informal)
[Explainability for NLP in Pharmacovigilance: A Study on Adverse Event Report Triage in Swedish](https://aclanthology.org/2025.cl4health-1.5/) (Dürlich et al., CL4Health 2025)
ACL