Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing.

Journal: JAMA psychiatry

PMID: 27626235

Abstract

IMPORTANCE: Suicide represents the 10th leading cause of death across age groups in the United States (12.6 cases per 100 000) and remains challenging to predict. While many individuals who die by suicide are seen by physicians before their attempt, they may not seek psychiatric care.

Authors

Thomas H McCoy

Center for Quantitative Health, Department of Psychiatry and Department of Medicine, Massachusetts General Hospital, Boston, MA. Electronic address: thmccoy@partners.org.
Victor M Castro
Ashlee M Roberson

Center for Experimental Drugs and Diagnostics, Center for Human Genetic Research and Department of Psychiatry, Massachusetts General Hospital, Boston.
Leslie A Snapper

Center for Experimental Drugs and Diagnostics, Center for Human Genetic Research and Department of Psychiatry, Massachusetts General Hospital, Boston.
Roy H Perlis

Center for Quantitative Health, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.

Keywords

Academic Medical Centers Accidents Adult Aged Aged, 80 and over Cause of Death Cohort Studies Female Follow-Up Studies Hospitals, General Humans Male Massachusetts Middle Aged Narration Natural Language Processing Patient Discharge Proportional Hazards Models Regression Analysis Retrospective Studies Risk Assessment Suicide

External Resources

View on PubMed Access via DOI PubMed (27626235)

Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing.

Abstract

Authors

Keywords

External Resources

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