Transferable Self-Harm Surveillance from Emergency Department Triage Notes Using an Evidence-Augmented Machine Learning Approach
- Published
- Source
- arXiv
- Paper number
- 304
- Field
- LLMs / NLP
- arXiv ID
- 2606.02545
Key points
- The approach achieved AUPRC scores of 0.887 ± 0.016 and 0.884 ± 0.012 on internal and external validation, respectively.
- Prospectively, it achieved an AUPRC of 0.881 ± 0.008 at the development institution and 0.879 ± 0.012 and 0.816 ± 0.015 at two external institutions without site-specific retraining.
- Its key advantage is that it can identify major self-harm methods with 95 percent accuracy, supporting more granular surveillance beyond binary classification.
Paper links
External research summaries. These are not HDATF publications or measured product results.