ArticleBMJ health & care informatics2025
Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports.
Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Fine-tuning and evaluating large language models for patient safety tasks: classification of contributing factors in incident reports.Journal of the American Medical Informatics Association : JAMIA · 2026Article
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2 authors.
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Abstract
objectiveTo evaluate the transferability of BERT (Bidirectional Encoder Representations from Transformers) to patient safety, we use it to classify incident reports characterised by limited data and encompassing multiple imbalanced classes.
methodsBERT was applied to classify 10 incident types and 4 severity levels by (1) fine-tuning and (2) extracting word embeddings for feature representation. Training datasets were collected from a state-wide incident reporting system in Australia (
resultsFine-tuned BERT outperformed small CNNs trained with BERT embedding and static word embeddings developed from scratch. The default parameters of BERT were found to be the most optimal configuration. For incident type, fine-tuned BERT achieved high F-scores above 89% across all test datasets ( DISCUSSION: Fine-tuned BERT led to improved performance, particularly in identifying rare classes and generalising effectively to unseen data, compared with small CNNs.
conclusionFine-tuned BERT may be useful for classification tasks in patient safety where data privacy, scarcity and imbalance are common challenges.
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