Predicting Suicide Among US Veterans Using Natural Language Processing-enriched Social and Behavioral Determinants of Health

Author:

Mitra Avijit1,Chen Kun2,Liu Weisong3,Kessler Ronald C.4,Yu Hong1

Affiliation:

1. University of Massachusetts Amherst

2. University of Connecticut

3. University of Massachusetts Lowell

4. Harvard Medical School

Abstract

Abstract Despite recognizing the critical association between social and behavioral determinants of health (SBDH) and suicide risk, SBDHs from unstructured electronic health record (EHR) notes for suicide predictive modeling 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 within 7, 30, 90, and 180 days of discharge. Using EHR data of 2,987,006 Veterans between October 1, 2009, and September 30, 2015, from the US Veterans Health Administration (VHA), we designed a case-control study that demonstrates that incorporating structured and NLP-extracted SBDH significantly enhances the performance of three architecturally distinct suicide predictive models - elastic-net logistic regression, random forest (RF), and multilayer perceptron. For example, RF achieved notable improvements in suicide prediction within 180 days of discharge, with an increase in the area under the receiver operating characteristic curve from 83.57–84.25% (95% CI = 0.63%-0.98%, p-val < 0.001) and the area under the precision recall curve from 57.38–59.87% (95% CI = 3.86%-4.82%, p-val < 0.001) after integrating NLP-extracted SBDH. These findings underscore the potential of NLP-extracted SBDH in enhancing suicide prediction across various prediction timeframes, offering valuable insights for healthcare practitioners and policymakers.

Publisher

Research Square Platform LLC

Reference60 articles.

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2. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015;Wang H;Lancet,2016

3. Suicide. https://www.who.int/news-room/fact-sheets/detail/suicide.

4. 2021 National Veteran Suicide Prevention Annual Report. Office of Mental Health and Suicide Prevention (2021).

5. Contact With Mental Health Services Prior to Suicide: A Systematic Review and Meta-Analysis;Walby FA;Psychiatr Serv,2018

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