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 point of contact, provide a succinct summary of presentations and an opportunity to identify self-harm. We developed a three-stage approach, augmenting traditional machine learning with large language model-based screening and evidence extraction to detect self-harm in ED triage notes. We assessed model transferability ac
The rapid advancement of large language models and their increasing integration into healthcare alongside existing machine learning techniques makes this type of application feasible now.
This development allows for earlier and more sensitive detection of critical public health issues like self-harm, improving intervention capabilities and potentially reducing adverse outcomes.
The ability to leverage unstructured clinical notes with AI for public health surveillance shifts from reactive diagnostic code analysis to proactive real-time risk identification.
- · Public Health Agencies
- · Healthcare Providers
- · AI/ML Healthcare Solutions
- · Traditional Surveillance Methods
- · Manual Triage Process
Improved early detection of self-harm in emergency departments leads to more timely interventions.
The successful application in self-harm diagnosis could accelerate AI adoption for other critical health risk identifications from unstructured medical data.
Ethical and privacy concerns around AI monitoring of sensitive patient data might intensify, leading to new regulatory frameworks for clinical AI deployments.
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Read at arXiv cs.CL