Researchers trained an AI system on routine admission information to flag people at higher risk of suicide-related behaviour soon after an emergency visit.
Emergency departments are a key point of contact for people at risk, but standard suicide screening can be hard to carry out consistently because it can be time-consuming and depends on what patients disclose. This study evaluated whether AI could make risk prediction more scalable by using routine triage data collected at admission.
The researchers compared five supervised learning approaches and assessed performance with multiple measures, including decision-focused metrics and interpretability tests. They report that short-term suicide-related behaviour risk can be predicted using structured triage information available at presentation, with the LightGBM model performing most favourably among those evaluated.
AI model predicts 30-day risk
The study developed and evaluated five AI models—logistic regression, random forest, XGBoost, LightGBM, and a multilayer perceptron—to predict suicide-related behaviour within 30 days using routinely collected structured ED triage data available at admission. On the independent test set, the LightGBM model showed AUROC 0.88, AUPRC 0.25, Recall 0.79, and an F2-score of 0.41. Model interpretation using SHAP pointed to prior psychiatric history, age, and physiological variables as important contributors. The authors also report that threshold optimisation and decision-curve analysis supported clinical utility, and they describe the approach as decision-support for prioritisation, follow-up planning, and early intervention.
