Machine Learning Prediction of Suicide Risk Does Not Identify Patients Without Traditional Risk Factors.

ABSTRACT: Objective: To determine whether predictions of suicide risk from machine learning models identify unexpected patients or patients without medical record documentation of traditional risk factors.Methods: The study sample included 27,091,382 outpatient mental health (MH) specialty or general medical visits with a MH diagnosis for patients aged 11 years or older from January 1, 2009, to September 30, 2017. We used predicted risk scores of suicide attempt and suicide death, separately, within 90 days of visits to classify visits into risk score percentile strata. For each stratum, we calculated counts and percentages of visits with traditional risk factors, including prior self-harm diagnoses and emergency department visits or hospitalizations with MH diagnoses, in

SUBMITTER: Cruz M 

PROVIDER: S-EPMC10270326 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature