Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions - Boudreaux, Edwin D.; Rundensteiner, Elke; Liu, Feifan; Wang, Bo; Larkin, Celine; Agu, Emmanuel; Ghosh, Samiran; Semeter, Joshua; Simon, Gregory; Davis-Martin, Rachel E. (Frontiers in Psychiatry, 2021)
Objective. Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model’s performance continuously improves over time by learning from newly available data. Method. This concept paper explores how machine learning approaches applied to healthcare data obtained from Electronic Health Records (EHRs), including billing and claims data, can advance our ability to accurately predict future suicidal behavior. Results. We provide a general overview of machine learning concepts, summarize exemplar studies, describe continued challenges, and propose innovative research directions. Conclusion. Machine learning has potential for improving estimation of suicide risk, yet important challenges and opportunities remain. Further research can focus on incorporating evolving methods for addressing data imbalances, understanding factors that affect generalizability across samples and healthcare systems, expanding the richness of the data, leveraging newer machine learning approaches, and developing automatic learning systems.