Machine Learning Algorithms in Suicide Prevention

Abstract

Clinicians overwhelmingly reported that suicide risk flags in electronic medical records would alter their clinical decision making. However, clinicians' likelihood of acting in response to a suicide risk flag was tied to which features were highlighted rather than the presence of the risk flag alone. Thus, the utility of a suicide risk algorithm will be reduced if clinical features underlying the algorithm are hidden from clinicians or if clinicians do not view the clinical features as intuitively meaningful predictors of suicide risk.