ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2026
Leveraging Large Language Models for Adverse Drug Event Detection: A Comparative Study of Token and Span-Based Named Entity Recognition.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Large Language Models in Adverse Drug Reaction Detection and Pharmacovigilance: A Systematic Review of Current Applications, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
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3 authors.
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Abstract
Adverse Drug Events (ADEs) pose a persistent threat to patient safety and public health. This study investigates the use of large language models (LLMs) fine-tuned for both token classification and span-based named entity recognition (NER) to improve ADE detection in clinical text. Using the 2018 n2c2 Track 2 dataset, we evaluate models under both predefined (gold label) and end-to-end settings. RoBERTa Large consistently outperforms other models, particularly in identifying ADEs, which remain more challenging due to their contextual ambiguity. Token-based models generally deliver stronger performance than span-based approaches, and ensemble methods, especially majority voting and XGBoost-based aggregation, further enhance end-to-end relation extraction by mitigating individual model weaknesses. These findings highlight the potential of fine-tuned LLMs, augmented by strategic ensembling, to advance clinical NLP pipelines and support safer, more personalized healthcare.
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Registered trials
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