Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System

Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need suc...

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Bibliographic Details
Main Authors: Ronald C. Kessler, Mark S. Bauer, Todd M. Bishop, Olga V. Demler, Steven K. Dobscha, Sarah M. Gildea, Joseph L. Goulet, Elizabeth Karras, Julie Kreyenbuhl, Sara J. Landes, Howard Liu, Alex R. Luedtke, Patrick Mair, William H. B. McAuliffe, Matthew Nock, Maria Petukhova, Wilfred R. Pigeon, Nancy A. Sampson, Jordan W. Smoller, Lauren M. Weinstock, Robert M. Bossarte
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-05-01
Series:Frontiers in Psychiatry
Subjects:
intensive case management
machine learning
predictive analytics
suicide
super learner
Online Access:https://www.frontiersin.org/article/10.3389/fpsyt.2020.00390/full

Internet

https://www.frontiersin.org/article/10.3389/fpsyt.2020.00390/full

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