Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

Journal: Medical care

PMID: 33438884

Abstract

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 increasing.

Authors

Juliet B Edgcomb

Semel Institute for Neuroscience & Human Behavior, David Geffen School of Medicine at UCLA, Los Angeles, CA.
Rohith Thiruvalluru

Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY.
Jyotishman Pathak

Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
John O Brooks

Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY.

Keywords

Adult Aged Algorithms Cohort Studies Electronic Health Records Female Hospitalization Humans Mental Disorders Middle Aged Patient Readmission Reproducibility of Results Retrospective Studies Risk Factors Self-Injurious Behavior Sensitivity and Specificity Suicide, Attempted Supervised Machine Learning Women Young Adult

External Resources

View on PubMed Access via DOI PubMed (33438884)

Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

Abstract

Authors

Keywords

External Resources

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