AIMC Topic: Suicide, Attempted

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Large-scale Text Mining of Suicide Attempt improves Identification of Distinct Suicidal Events in Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
In this study, we explore a natural language processing (NLP) algorithm's capacity to identify proximal but distinct suicide attempt (SA) events compared to diagnostic code-based approaches. This study used an NLP algorithm with high precision in ide...
Electronic Health Records Suicide, Attempted Natural Language Processing Data Mining Humans Algorithms
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Statistical and artificial intelligence techniques to identify risk factors for suicide in children and adolescents.

Science progress Jan 1, 2023
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...
Retrospective Studies Risk Factors Suicide, Attempted Adolescent Humans Artificial Intelligence Suicidal Ideation Child
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Interpretable Estimation of Suicide Risk and Severity from Complete Blood Count Parameters with Explainable Artificial Intelligence Methods.

Psychiatria Danubina Jan 1, 2023
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...
Humans Artificial Intelligence Female Blood Cell Count Risk Assessment Reproducibility of Results Suicide, Attempted Young Adult Adolescent Aged Case-Control Studies Adult Suicidal Ideation Aged, 80 and over Male Suicide Middle Aged Models, Statistical
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Natural language processing of medical records: new understanding of suicide ideation by dementia subtypes.

International psychogeriatrics Apr 1, 2022
Humans Suicide, Attempted Medical Records Suicidal Ideation Dementia Natural Language Processing
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Translating promise into practice: a review of machine learning in suicide research and prevention.

The lancet. Psychiatry Mar 1, 2022
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...
Suicide, Attempted Suicidal Ideation Machine Learning Research Design Decision Support Techniques Humans
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Predicting Sex-Specific Nonfatal Suicide Attempt Risk Using Machine Learning and Data From Danish National Registries.

American journal of epidemiology Dec 1, 2021
Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts might offer opportunities for prevention. This case-cohort study used machine learning to examine sex-specific risk profiles for suicide attempts in Dani...
Adolescent Health Status Denmark Registries Sociodemographic Factors Emigrants and Immigrants Mental Health Male Middle Aged Suicide, Attempted Humans Risk Factors Young Adult Machine Learning Adult Mental Disorders Female
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Identification of Suicide Attempt Risk Factors in a National US Survey Using Machine Learning.

JAMA psychiatry Apr 1, 2021
IMPORTANCE: Because more than one-third of people making nonfatal suicide attempts do not receive mental health treatment, it is essential to extend suicide attempt risk factors beyond high-risk clinical populations to the general adult population.
Humans Health Surveys United States Machine Learning Longitudinal Studies Female Male Risk Factors Middle Aged Aged, 80 and over Suicide, Attempted Aged Young Adult Adolescent Adult
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Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

Medical care Feb 1, 2021
BACKGROUND: Suicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increas...
Self-Injurious Behavior Women Humans Algorithms Suicide, Attempted Mental Disorders Cohort Studies Retrospective Studies Supervised Machine Learning Patient Readmission Risk Factors Adult Hospitalization Sensitivity and Specificity Aged Middle Aged Young Adult Electronic Health Records Female Reproducibility of Results
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What health records data are required for accurate prediction of suicidal behavior?

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2019
OBJECTIVE: The study sought to evaluate how availability of different types of health records data affect the accuracy of machine learning models predicting suicidal behavior.
Suicide, Attempted Logistic Models Female Male Risk Factors Surveys and Questionnaires Self Report Humans Aged Middle Aged Adolescent Models, Psychological Machine Learning Risk Assessment Adult Suicide Electronic Health Records
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