Artificial intelligence approach for predicting suicide-related behaviour in emergency departments
- 1Centre for Applied Data Science, University of Johannesburg
- 2Department of Applied Information Systems, University of Johannesburg
- 3Department of Nursing, University of Johannesburg
Abstract
Suicide remains a major public health challenge, with emergency departments (EDs) representing a critical point of contact for individuals at risk. However, conventional suicide screening approaches are often time-intensive, disclosure-dependent, and difficult to implement consistently in high-acuity settings. This study developed and evaluated an artificial intelligence (AI)-based model for predicting suicide-related behaviour (SRB) within 30 days of ED presentation using routinely collected structured triage data available at admission. Five supervised learning models, logistic regression, random forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and a multilayer perceptron (MLP), were developed and evaluated using patient-level train-validation-test splits. Performance was assessed using a multidimensional framework including area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), Recall, F2-score, Matthews Correlation Coefficient (MCC), Balanced Accuracy, threshold optimisation, decision-curve analysis, bootstrap confidence intervals, and SHapley Additive exPlanations (SHAP). Among the evaluated models, LightGBM showed the most favourable balance between overall discrimination, minority-class discrimination, sensitivity, and decision stability, achieving an AUROC of 0.88, an AUPRC of 0.25, a Recall of 0.79, and an F2-score of 0.41 on the independent test set. Threshold optimisation and decision-curve analysis further supported the model’s clinical utility, while SHAP analyses identified prior psychiatric history, age, and physiological variables as important contributors to risk prediction. These findings demonstrate that short-term SRB risk can be predicted using structured ED triage information available at presentation. Rather than replacing clinicians, the proposed model aims to provide transparent, scalable decision support to support prioritisation, follow-up planning, and early intervention. Although external validation and prospective studies remain necessary, the study provides a deployment-oriented framework for integrating AI-assisted suicide risk prediction into emergency care workflows.
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BibTeX
@article{mokheleli2026artificial,
title = {{Artificial intelligence approach for predicting suicide-related behaviour in emergency departments}},
author = {Tsholofelo Mokheleli and Tebogo Makaba and Patrick Ndayizigamiye and Nompumelelo Ndlovu},
journal = {Scientific Reports},
year = {2026},
month = aug,
doi = {10.1038/s41598-026-64073-y},
url = {https://doi.org/10.1038/s41598-026-64073-y},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence approach for predicting suicide-related behaviour in emergency departments
AU - Tsholofelo Mokheleli
AU - Tebogo Makaba
AU - Patrick Ndayizigamiye
AU - Nompumelelo Ndlovu
PY - 2026
DA - 2026/08/01
JO - Scientific Reports
DO - 10.1038/s41598-026-64073-y
UR - https://doi.org/10.1038/s41598-026-64073-y
AB - Suicide remains a major public health challenge, with emergency departments (EDs) representing a critical point of contact for individuals at risk. However, conventional suicide screening approaches are often time-intensive, disclosure-dependent, and difficult to implement consistently in high-acuity settings. This study developed and evaluated an artificial intelligence (AI)-based model for predicting suicide-related behaviour (SRB) within 30 days of ED presentation using routinely collected structured triage data available at admission. Five supervised learning models, logistic regression, random forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and a multilayer perceptron (MLP), were developed and evaluated using patient-level train-validation-test splits. Performance was assessed using a multidimensional framework including area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), Recall, F2-score, Matthews Correlation Coefficient (MCC), Balanced Accuracy, threshold optimisation, decision-curve analysis, bootstrap confidence intervals, and SHapley Additive exPlanations (SHAP). Among the evaluated models, LightGBM showed the most favourable balance between overall discrimination, minority-class discrimination, sensitivity, and decision stability, achieving an AUROC of 0.88, an AUPRC of 0.25, a Recall of 0.79, and an F2-score of 0.41 on the independent test set. Threshold optimisation and decision-curve analysis further supported the model’s clinical utility, while SHAP analyses identified prior psychiatric history, age, and physiological variables as important contributors to risk prediction. These findings demonstrate that short-term SRB risk can be predicted using structured ED triage information available at presentation. Rather than replacing clinicians, the proposed model aims to provide transparent, scalable decision support to support prioritisation, follow-up planning, and early intervention. Although external validation and prospective studies remain necessary, the study provides a deployment-oriented framework for integrating AI-assisted suicide risk prediction into emergency care workflows.
SN - 2045-2322
ER -