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This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (1246) Clinical Informatics (2126) Natural Language Processing (1224) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (257) Electronic Health Records (1651) Machine Learning (3030) Research Methodology - AI (147) Applications of AI (838)

Published on in Vol 9 (2025)

This is a member publication of Bibsam Consortium

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71949, first published .
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Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study

Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study

Authors of this article:

Elizaveta Kopacheva1 Author Orcid Image ;   Aron Henriksson2 Author Orcid Image ;   Hercules Dalianis2 Author Orcid Image ;   Tora Hammar3 Author Orcid Image ;   Alisa Lincke1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. De Micco F, Palma G, Seveso G, Giacomobono F, Scendoni R, Tambone V. AI-Driven Approaches for Adverse Event Detection: A Systematic Review of Current Evidence. Safety 2026;12(2):52 View
  2. Šuvalov H, Umov N, Malk M, Haug M, Laur S, Oja M, Tamm S, Reisberg S, Vilo J, Kolde R. Extracting and Classifying Drug Discontinuations From Estonian Electronic Health Records: Development and Validation Study. Journal of Medical Internet Research 2026;28:e86183 View
  3. Schreier O, Yazdani A, Galdadas I, Kabak R, Gervasio F, Mu G, Teodoro D. Application of Language Models for the Analysis of Adverse Drug Events in Pharmaceutical Research and Development: Scoping Review. JMIR AI 2026;5:e77732 View