Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention.

ABSTRACT:

Importance

The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information.

Objective

To determine whether model prediction could be improved by adding information extracted from clinical notes and public records.

Design, setting, and participants

Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short

SUBMITTER: Kessler RC 

PROVIDER: S-EPMC9857842 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature