BACKGROUND: South Korea has the highest suicide rate among the Organisation for Economic Co-operation and Development nations, with particularly elevated figures among persons with disabilities. Research has shown a strong correlation between suicida...
Epidemiology and psychiatric sciences
Oct 20, 2025
AIMS: The epidemiology and age-specific patterns of lifetime suicide attempts (LSA) in China remain unclear. We aimed to examine age-specific prevalence and predictors of LSA among Chinese adults using machine learning (ML).
BACKGROUND: Insomnia is a significant independent risk factor for depression and suicidality. However, these conditions often go undetected, particularly in individuals presenting with sleep complaints. This study aimed to develop and validate machin...
Advances in artificial intelligence (AI) technologies sparked a rapid development of smartphone applications designed to help individuals experiencing mental health problems through an AI-powered chatbot agent. However, the safety of such agents when...
Suicide is a tragedy for family and society. With social media becoming an integral part of people's life nowadays, assessing suicidal risk based on one's social media behavior has drawn increasing research attentions. The majority of the works train...
Suicide prevention is currently a global public health priority, since suicide has been a prevalent cause of death or potential loss of life. Multiple factors contribute to suicide risk, such as depression and a history of attempted suicide among oth...
Cognitive processes preceding suicidal attempts (SA) remain poorly understood, particularly in distinguishing those who act on suicidal thoughts from those who do not. This study compared proximal contemplation and planning processes during suicidal ...
BACKGROUND: Recently, the machine learning (ML) methods have been recommended to predict suicide attempts (SA). However, there is little literature reported the prediction models based on multiple machine learning methods of Chinese people and previo...
Traditional clinical risk assessment tools proved inadequate for reliably identifying individuals at high risk for suicidal behavior. As a result, machine learning (ML) techniques have become progressively incorporated into psychiatric care. This stu...
Young adults with childhood sexual abuse (CSA) are an especially vulnerable group to suicide. Suicide encompasses different phases, but for CSA survivors the salient factors precipitating suicide are rarely studied. In this study, from a progressive ...
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