Optimising pain identification in resource-limited emergency departments using transfer learning and fine-tuned language models.

OBJECTIVE: To optimise the identification of patients presenting with pain in emergency department (ED) settings with limited resources using multiple transfer learning techniques. METHODS: Two strategies were explored: (1) fine-tuning a pre-trained language model, previously fine-tuned on data from a well-resourced ED, using labelled data from a target ED, and (2) continual pre-training using task-specific unlabelled data to enhance clinical text classification. RESULTS: With 2000 labelled samples from a target ED, the combined strategies achieved an F1-score of 92%, demonstrating significant benefits of transfer learning in resource-constrained settings. DISCUSSION: Accurately identifying pain in patients upon arrival to the ED is crucial for timely and effective treatment. Findings suggest that combining both transfer learning strategies can significantly enhance pain identification performances in resource-constrained settings. CONCLUSION: Combining fine-tuning on labelled data and continual pre-training on unlabelled data has potential to optimise model performance in both resource-constrained and well-resourced settings, highlighting the broader applicability and potential of these techniques for improving clinical text classification.