Abstract Machine learning is increasingly used in mental health research and has the
potential to advance our understanding of how to characterize, predict, and treat mental …

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 …

For years, the scientific community has researched monitoring approaches for the detection
of certain mental disorders and risky behaviors, like depression, eating disorders, gambling …

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

Background Risk assessments are a central component of mental health care. Few national
studies have been done in the UK on risk assessment tools used in mental health services …

Background Suicide is a major public health concern globally. Accurately predicting suicidal
behavior remains challenging. This study aimed to use machine learning approaches to …

Background Suicide is one of the leading causes of death in the USA and population risk
prediction models can inform decisions on the type, location, and timing of public health …

Background Suicide is one of the main preventable causes of death. Artificial intelligence
(AI) could improve methods for assessing suicide risk. The objective of this review is to …

Importance Numerous prognostic models of suicide risk have been published, but few have
been implemented outside of integrated managed care systems. Objective To evaluate …

Background Increasingly, patient medication adherence data are being consolidated from
claims databases and electronic health records (EHRs). Such databases offer an indirect …