AIMC Topic: Suicide, Attempted

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Age-specific prevalence and predictors of lifetime suicide attempts using machine learning in Chinese adults: a nationwide multi-centre survey.

Epidemiology and psychiatric sciences
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).

Integrating multiple feature assessment methods to identify key predictors of repeat suicide attempts in Taiwan.

BMC psychiatry
BACKGROUND: The high rate of repeat attempts among individuals who have previously attempted suicide presents a critical challenge in public health and suicide prevention. While early and targeted intervention is crucial for this high-risk group, eff...

Predicting survival factor following suicide attempt in Iran: an ensemble machine learning technique.

BMC psychiatry
BACKGROUND: Suicide represents a significant challenge to public health that calls for a suitable intervention from the healthcare sector. Despite the typically low suicide rate among most Muslim nations, research indicates that there is an increase ...

Interpretable Machine Learning approach for predicting clinically significant suicide risk: A case study of patients with major depressive disorder in Greece.

Psychiatry research
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...

Before the attempt: How people think and plan in suicide crises.

Journal of affective disorders
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 ...

The concise machine learning prediction models for suicide attempt in China: Based on demographic and social factors.

Journal of affective disorders
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...

Decoding vital variables in predicting different phases of suicide among young adults with childhood sexual abuse: a machine learning approach.

Translational psychiatry
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 ...

Unveiling sex difference in factors associated with suicide attempt among Chinese adolescents with depression: a machine learning-based study.

Journal of mental health (Abingdon, England)
BACKGROUND: Adolescents with depression are at heightened risk of suicide, with a distinct sex difference in suicidal behaviour observed. This study explores the sex-specific factors influencing suicide attempts among Chinese adolescents with depress...

Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention.

PloS one
INTRODUCTION: Autistic people experience higher risk of suicidal ideation (SI) and suicide attempts (SA) compared to non-autistic people, yet there is limited understanding of complex, multilevel factors that drive this disparity. Further, determinan...

Development and validation of short-term, medium-term, and long-term suicide attempt prediction models based on a prospective cohort in Korea.

Asian journal of psychiatry
BACKGROUND: This study aimed to develop and validate prediction models for short-(3 months), medium-(1 year), and long-term suicide attempts among high-risk individuals in South Korea.