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 …

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 …

Suicide and depression are on the upswing in today's world. Stress, anxiety attacks,
depressive disorders, and other factors are the most common causes of suicidal thoughts in …

Current suicide risk assessments for predicting suicide attempts are time consuming, of low
predictive value and have inadequate reliability. This paper aims to develop a predictive …

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 …

Abstract According to WHO (World Health Organization), every 40 seconds a person dies of
suicide. This amounts to a total of 800,000 people every year falling victim to suicides …

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 …

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 …

Suicidal thoughts and behaviors are an international public health problem contributing to
800,000 annual deaths and up to 25 million nonfatal suicide attempts. In the United States …

Objective We aimed to develop predictive models to identify suicide attempters among
individuals with suicide ideation using a machine learning algorithm. Methods Among …