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, despite consistent core themes such as self-poisoning and self-injury. These documentation differences are associated with reduced cross-site performance. Our findings provide insight into how institutional variation affects the identification of self-harm in clinical text and highlight potential methods to improve model generalisability.
| Comments: | Accepted to CLPsych2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.01678 [cs.CL] |
| (or arXiv:2606.01678v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.01678 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Liuliu Chen [view email]
[v1]
Mon, 1 Jun 2026 04:37:58 UTC (430 KB)