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
.
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
Journals
- 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
- 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
- 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
- Guillot J, Miao B, Suresh A, Sushil M, Williams C, Vashisht R, Oskotsky T, Sirota M, Butte A, Cheungpasitporn W. Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T cell therapy using large language models. PLOS Digital Health 2026;5(8):e0001426 View
Books/Policy Documents
- Lynch N, McHugh M, Loughran R, McCaffrey F, Kowalski D. Artificial Intelligence and Cognitive Science. View