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 …

Objective: Early identification of individuals who are at risk for suicide is crucial in supporting
suicide prevention. Machine learning is emerging as a promising approach to support this …

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

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 …

Suicide is a significant public health concern, and there is growing interest in using machine
learning techniques to identify people who are at a high risk of committing suicide. In this …

Suicide causes approximately one death every 40 s. Suicidal ideation is the first stage in the
risk scale, being a potential gate for suicide prevention. Machine learning emerged as a …

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 …

For decades, our ability to predict suicide has remained at near‐chance levels. Machine
learning has recently emerged as a promising tool for advancing suicide science …

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 …