Why Do Self-Harm Prediction Models Struggle to Generalise? Lexical and Semantic Variations in Emergency Department Triage Notes
arXiv:2606.01678v1 Announce Type: new Abstract: Self-harm presentations to emergency departments (EDs) are strongly associated with higher suicide risk. NLP models have shown robust performance in detecting self-harm from triage notes within single hospitals, yet performance often declines across institutions. To examine potential causes, we compare ED triage notes from two hospitals by analyzing lexical characteristics, highly associated predictive features, and salient topics. Our results reveal variation in lexical expression and feature importance related to self-harm across hospitals, des
The proliferation of AI models in healthcare, particularly in sensitive areas like mental health, is revealing inherent challenges in their real-world deployment and generalizability across diverse data environments.
This research highlights a critical limitation of AI models trained on localized data, demonstrating performance degradation when applied to new institutions, which has significant implications for AI deployment scalability and reliability in healthcare.
The understanding that AI models for sensitive applications like self-harm prediction require more robust, generalizable training data and methods to account for lexical and semantic variations across institutions.
- · AI model generalizability researchers
- · Healthcare institutions with diverse data sets
- · Patients benefiting from more robust AI predictions
- · AI model developers ignoring data heterogeneity
- · Healthcare systems relying on single-source AI solutions
- · Patients harmed by non-generalizable AI predictions
Increased focus on federated learning and transfer learning techniques in healthcare AI.
Development of industry standards for bias detection and generalizability testing for healthcare AI models.
Potential for healthcare AI regulation to include mandates for multi-institutional validation and lexical variation accounting.
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