Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records

Mar 1, 2020 | Publications

translational psychiatry


Summary: Suicide is taking our children, plaguing our veterans and poisoning troubled minds as a viable alternative to seeking help. It is the tenth leading cause of death in the US, claiming the lives of more than 47,000 individuals in 2017, twice as many deaths as from homicide. It’s the second leading cause of death among individuals between the ages of 10 and 34.[1]

An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts and put information in the hands of providers who can act upon that information.

In this study we developed an EWS for high-risk suicide attempt patients with advanced machine-learning algorithms and deep neural networks applied to data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively.

The suicide attempt rate in the “very high risk” category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.