AIMC Topic: Suicidal Ideation

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Statistical and artificial intelligence techniques to identify risk factors for suicide in children and adolescents.

Science progress
BACKGROUND: Suicidal Behaviors and Thoughts are a relevant public health issue that includes suicidal ideation, non-suicidal self-harm, attempted suicide, and failed suicides. Since there is a progression of suicidal behaviors, whereby suicide is mor...

Interpretable Estimation of Suicide Risk and Severity from Complete Blood Count Parameters with Explainable Artificial Intelligence Methods.

Psychiatria Danubina
BACKGROUND: The peripheral inflammatory markers are important in the pathophysiology of suicidal behavior. However, methods for practical uses haven't been developed enough yet. This study developed predictive models based on explainable artificial i...

Translating promise into practice: a review of machine learning in suicide research and prevention.

The lancet. Psychiatry
In ever more pressured health-care systems, technological solutions offering scalability of care and better resource targeting are appealing. Research on machine learning as a technique for identifying individuals at risk of suicidal ideation, suicid...

Imputation and characterization of uncoded self-harm in major mental illness using machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We aimed to impute uncoded self-harm in administrative claims data of individuals with major mental illness (MMI), characterize self-harm incidence, and identify factors associated with coding bias.

Identifying Suicidal Adolescents from Mental Health Records Using Natural Language Processing.

Studies in health technology and informatics
Suicidal ideation is a risk factor for self-harm, completed suicide and can be indicative of mental health issues. Adolescents are a particularly vulnerable group, but few studies have examined suicidal behaviour prevalence in large cohorts. Electron...