Abstract
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, 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.
Highlights
According to the World Health Organization, approximately 800,000 people die by suicide annually worldwide, making it the 18th leading cause of death [1]1
The largest meta-analysis of suicide prediction [4] analyzed 365 studies and concluded that predictions based on individual risk or protective factors have led to weak predictive accuracy showing little improvement over time
This paper provides an overview of machine learning applied to suicide prediction, summarizes exemplar published studies for illustration, and explores future directions for research
Summary
According to the World Health Organization, approximately 800,000 people die by suicide annually worldwide, making it the 18th leading cause of death [1]1. In the United States, 48,344 people died by suicide in 2018 [2]2, making it the tenth leading cause of death and contributing to decreasing average United States life expectancy [3]. Suicide is an uncommon event, even among those considered at high risk, such as individuals who have been psychiatrically hospitalized, making it inherently difficult to predict. Suicide results from a complex interaction of numerous factors, each having small but meaningful contributions, rather than a handful of powerful stable predictors. Previous efforts have not collected sufficiently comprehensive chronic and transient risk factors over time within a sufficiently large sample to produce accurate prediction models
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