Background The objective was to develop and assess performance of an algorithm
predicting suicide-related ICD codes within three months of psychiatric discharge. Methods …

Background Few studies report on machine learning models for suicide risk prediction in
adolescents and their utility in identifying those in need of further evaluation. This study …

Introduction Suicidal thoughts and suicide attempts are one of the most prominent public
health concerns in adolescents and therefore early detection is important to initiate …

Objective The rapid proliferation of machine learning research using electronic health
records to classify healthcare outcomes offers an opportunity to address the pressing public …

BackgroundMobile technology offers unique opportunities for monitoring short-term suicide
risk in daily life. In this study of suicidal adolescent inpatients, theoretically informed risk …

Accurate prediction of suicide risk among children and adolescents within an actionable
time frame is an important but challenging task. Very few studies have comprehensively …

Background The use of machine learning (ML) algorithms to study suicidality has recently
been recommended. Our aim was to explore whether ML approaches have the potential to …

Objective To obtain predictors of suicidal ideation, which can also be used for an indirect
assessment of suicidal ideation (SI). To create a classifier for SI based on variables of the …

Objective: The purpose of this article was to determine whether longitudinal historical data,
commonly available in electronic health record (EHR) systems, can be used to predict …

Background Adolescents have high rates of nonfatal suicide attempts, but clinically practical
risk prediction remains a challenge. Screening can be time consuming to implement at …