AI Medical Compendium Topic

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Suicide

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Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study.

Frontiers in public health
BACKGROUND: Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms of negative attitudes were not adequately considered, and developed models lacked rigor...

Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.

Translational psychiatry
Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transportability, namely the performance of a model trained on one dataset and applied to different data, is ...

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.

Psychiatry research
Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic Topic Modeling, a natural language processing method that evaluates topic changes over time, to anal...

Anticipating influential factors on suicide outcomes through machine learning techniques: Insights from a suicide registration program in western Iran.

Asian journal of psychiatry
Suicide is a global public health concern, with increasing rates observed in various regions, including Iran. This study focuses on the province of Hamadan, Iran, where suicide rates have been on the rise. The research aims to predict factors influen...

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...

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...

Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients.

Journal of psychiatric research
Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made impactful suicide prevention advances, efforts primarily target high-risk patients with documented suic...

Global Suicide Mortality Rates (2000-2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data.

International journal of environmental research and public health
Suicide research is directed at understanding social, economic, and biological causes of suicide thoughts and behaviors. (1) Background: Worldwide, certain countries have high suicide mortality rates (SMRs) compared to others. Age-standardized suicid...

Detection of suicidality from medical text using privacy-preserving large language models.

The British journal of psychiatry : the journal of mental science
BACKGROUND: Attempts to use artificial intelligence (AI) in psychiatric disorders show moderate success, highlighting the potential of incorporating information from clinical assessments to improve the models. This study focuses on using large langua...

Predicting Suicides Among US Army Soldiers After Leaving Active Service.

JAMA psychiatry
IMPORTANCE: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions.