ArticleNpj mental health research2025
Post-discharge suicide prediction among US veterans using natural language processing-enriched social and behavioral determinants of health.
Article in Npj mental health research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans.medRxiv : the preprint server for health sciences · 2026Article
- Extracting Social Determinants of Health From Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods.JMIR medical informatics · 2026Article
- Life events extraction from healthcare notes for veteran acute suicide risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Predicting suicide death among veterans after psychiatric hospitalization using transformer based models with social determinants and NLP.Scientific reports · 2025Article
- Extracting social determinants of health from electronic health records: development and comparison of rule-based and large language models-based methods.medRxiv : the preprint server for health sciences · 2025Article
- Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
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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 characteristic curve increase from 83.57% to 84.25% (95% CI = 0.63%-0.98%, p val < 0.001) and area under the precision-recall curve increase from 57.38% to 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 advancing suicide prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.