ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025
Studying Veteran food insecurity longitudinally using electronic health record data and natural language processing.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Food insecurity is an important social risk factor that is directly linked to patient health and well-being. The Department of Veterans Affairs (VA) aims to identify and resolve food insecurity through social and clinical interventions. However, evaluating the impact of such interventions is made challenging by the lack of follow-up data on Veteran food insecurity status. One potential solution is to leverage documentation of food insecurity in electronic health records (EHRs). In this paper, we developed and validated a natural language processing system to identify food insecurity status from clinical notes and applied it to study longitudinal trajectories of food insecurity among a large cohort of food insecure Veterans. Our analyses provide insight into the timing and persistence of Veteran food insecurity; in the future, our methods will be used to evaluate food insecurity interventions and evaluate VA policy.
Identifiers
40502216PMC12150752What OpenQuestion holds
Registered trials
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