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AI models struggle to generalize self-harm prediction across hospitals

By PulseAugur Editorial · [3 sources] ·

Two new research papers explore the challenges and potential solutions for using NLP models to predict self-harm from emergency department triage notes. The first paper identifies lexical and semantic variations across different hospitals as a key reason for model generalizability issues. The second paper proposes an evidence-augmented machine learning approach, combining traditional methods with LLM-based screening, to improve model transferability across institutions and accurately identify self-harm methods. AI

IMPACT These studies highlight the need for more robust and transferable AI models in healthcare, particularly for sensitive applications like self-harm prediction, which could improve patient safety and resource allocation.

RANK_REASON Two academic papers published on arXiv detailing research into NLP models for self-harm prediction.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Liuliu Chen, Mike Conway, Jo Robinson, Vlada Rozova ·

    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 ofte…

  2. arXiv cs.CL TIER_1 English(EN) · Liuliu Chen, Gowri Rajaram, Eleanor Bailey, Katrina Witt, Michelle Lamblin, Jo Robinson, Mike Conway, Vlada Rozova ·

    Transferable Self-Harm Surveillance from Emergency Department Triage Notes Using an Evidence-Augmented Machine Learning Approach

    arXiv:2606.02545v1 Announce Type: new Abstract: Self-harm is a major public health concern, but current surveillance relying on hospital presentations is inadequate due to the low sensitivity of diagnostic codes. Emergency Department (ED) triage notes, recorded at the initial poi…

  3. arXiv cs.CL TIER_1 English(EN) · Vlada Rozova ·

    Transferable Self-Harm Surveillance from Emergency Department Triage Notes Using an Evidence-Augmented Machine Learning Approach

    Self-harm is a major public health concern, but current surveillance relying on hospital presentations is inadequate due to the low sensitivity of diagnostic codes. Emergency Department (ED) triage notes, recorded at the initial point of contact, provide a succinct summary of pre…

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