Post-discharge suicide prediction among US veterans using natural language processing-enriched social and behavioral determinants of health.

ABSTRACT: Despite the established association between social and behavioral determinants of health (SBDH) and suicide risk, SBDHs from unstructured electronic health record notes for suicide prediction remain underutilized. This study investigates the impact of SBDH identified from both structured and unstructured data utilizing a natural language processing (NLP) system on suicide prediction at 7, 30, 90, and 180 days post-discharge. Using data from 2,987,006 US Veterans between 1 October 2009, and 30 September 2015, we designed a case-control study demonstrating that structured and NLP-extracted SBDH significantly enhance distinct prediction models' performance. For example, the random forest model improved its 180-day post-discharge prediction with an area under the receiver operating characteris

SUBMITTER: Mitra A 

PROVIDER: S-EPMC11846906 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature