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

ABSTRACT: 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 such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the rem

SUBMITTER: Kessler RC 

PROVIDER: S-EPMC7219514 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature