The prediction of suicidal thought and behavior has met with mixed results. This study of
3,476 de-identified participants (4,019 before data exclusion) quantified the prediction of …

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

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 …

For decades, our ability to predict suicidal thoughts and behaviors (STBs) has been at near-
chance levels. The objective of this study was to advance prediction by addressing two …

Advances in artificial intelligence and machine learning have fueled growing interest in the
application of predictive analytics to identify high‐risk suicidal patients. Such application will …

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 Identifying predictors contributing to suicide risk could help prevent suicides via
targeted interventions. However, using only known risk factors may not yield accurate …

We thank Dr Roaldset for his thoughtful comments on our meta-analysis, and appreciate the
opportunity to discuss the important issue raised in Roaldset (2016). In his letter, Roaldset …

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 …

Suicide risk prediction models can identify individuals for targeted intervention. Discussions
of transparency, explainability, and transportability in machine learning presume complex …