Research has posited that machine learning could improve suicide risk prediction models,
which have traditionally performed poorly. This systematic review and meta-analysis …

Background Early detection and prediction of suicidal behaviour are key factors in suicide
control. In conjunction with recent advances in the field of artificial intelligence, there is …

BackgroundSuicide prediction models have been formulated in a variety of ways and are
heterogeneous in the strength of their predictions. Machine learning has been a proposed …

Abstract Machine learning (ML) has emerged as a promising tool to enhance suicidal
prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric …

Machine learning or artificial intelligence may afford opportunities in mental health research
and in the clinical care of suicidal patients. However, application of such techniques should …

Abstract Machine learning is being used to discover models to predict the progression from
suicidal ideation to action in clinical populations. While quantifiable improvements in …

Importance Suicide prediction models have the potential to improve the identification of
patients at heightened suicide risk by using predictive algorithms on large-scale data …

Theoretically-driven models of suicide have long guided suicidology; however, an approach
employing machine learning models has recently emerged in the field. Some have …

Objective: Suicide is a growing public health concern with a global prevalence of
approximately 800,000 deaths per year. The current process of evaluating suicide risk is …

Background The predictive accuracy of suicidal behaviour has not improved over the last
decades. We aimed to explore the potential of machine learning to predict future suicidal …