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Suicidal Ideation

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Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations.

International journal of mental health nursing
This research addresses the critical issue of identifying factors contributing to admissions to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. T...

Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning.

Scientific reports
Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online p...

Gender-specific factors of suicidal ideation among high school students in Yunnan province, China: A machine learning approach.

Journal of affective disorders
BACKGROUND: Suicidal ideation (SI) assumes a pivotal role in predicting suicidal behaviors. The incidence of SI among high (junior and senior) school students is significantly higher than that of other age groups. The aim of this study is to explore ...

Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide.

Journal of affective disorders
BACKGROUND: Strategies to detect the presence of suicidal ideation (SI) or characteristics of ideation that indicate marked suicide risk are critically needed to guide interventions and improve care during care transition periods. Some studies indica...

Suicidal behaviors among high school graduates with preexisting mental health problems: A machine learning and GIS-based study.

The International journal of social psychiatry
BACKGROUND: Suicidal behavior among adolescents with mental health disorders, such as depression and anxiety, is a critical issue. This study explores the prevalence and predictors of past-year suicidal behaviors among Bangladeshi high school graduat...

Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models.

BMC psychiatry
BACKGROUND: Suicidal behaviors, which may lead to death (suicide) or survival (suicide attempt), are influenced by various factors. Identifying the specific risk factors for suicidal behavior mortality is critical for improving prevention strategies ...

Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.

Journal of medical systems
Suicide constitutes a public health issue of major concern. Ongoing progress in the field of artificial intelligence, particularly in the domain of large language models, has played a significant role in the detection, risk assessment, and prevention...

Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation.

Journal of biomedical informatics
BACKGROUND: Suicide is a leading cause of death worldwide, making early identification of suicidal behaviors crucial for clinicians. Current Natural Language Processing (NLP) approaches for identifying suicidal behaviors in Electronic Health Records ...

Prediction of Suicidal Thoughts and Suicide Attempts in People Who Gamble Based on Biological-Psychological-Social Variables: A Machine Learning Study.

The Psychiatric quarterly
Recent research has shown that people who gamble are more likely to have suicidal thoughts and attempts compared to the general population. Despite the advancements made, no study to date has predicted suicide risk factors in people who gamble using ...