The current review provides a quantitative synthesis of the empirical literature on sleep
disturbance as a risk factor for suicidal thoughts and behaviors (STBs). A systematic search …

In ever more pressured health-care systems, technological solutions offering scalability of
care and better resource targeting are appealing. Research on machine learning as a …

In recent years, there has been a growing interest in understanding the relationship between
sleep and suicide. Although sleep disturbances are commonly cited as critical risk factors for …

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 …

Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of
research have produced predictions slightly better than chance (AUCs= 0.56–0.58). In this …

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 …

The use of machine learning is increasing in clinical psychology, yet it is unclear whether
these approaches enhance the prediction of clinical outcomes. Several studies show that …

Close to one million people commit suicide each year, with suicidal attempts being the main
risk factor for suicide. The aim of this systematic review and meta-analysis is to achieve a …

Importance The months after psychiatric hospital discharge are a time of high risk for suicide.
Intensive postdischarge case management, although potentially effective in suicide …