Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing

Abstract

Suicide represents one of the most dreaded outcomes of psychiatric illness. With 41 149 completed suicides reported by the US Centers for Disease Control and Prevention in 2013, suicide is the 10th leading cause of death in the United States (12.6 cases per 100 000) and the second leading cause among individuals aged 15 to 24 years (10.9 cases per 100 000).^1,2 While epidemiological investigations provide some guidance regarding demographic features, symptoms, or diagnoses associated with greatest risk, such studies tend to focus on suicide attempts among epidemiological cohorts assessed by diagnostic interviews.^3-5 Conversely, small-scale studies^6,7 have evaluated biomarkers or psychosocial features associated with suicide attempt among high-risk individuals. What is to our knowledge the sole larger study⁸ to examine death by suicide assessed a cohort of US soldiers after psychiatric hospital discharge, reporting on 68 deaths.