ArticleInternational journal of medical informatics2026
A topic modeling analysis of stigma dimensions, social, and related behavioral circumstances in clinical notes among patients with HIV.
Article in International journal of medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in HIV research: a structured review and task-oriented clinical framework.Frontiers in digital health · 2026Review
- Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
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8 authors.
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Abstract
objectiveTo characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV (PLWHs) seeking care, using natural language processing methods applied to a large collection of electronic health record (EHR) clinical notes from a large integrated health system in the southeast United States.
methodsWe identified a cohort of PLWHs from the University of Florida (UF) Health Integrated Data Repository and performed topic modeling analysis using Latent Dirichlet Allocation (LDA) to uncover stigma-related dimensions and related social and behavioral contexts. Domain experts created a seed list of HIV-related stigma keywords, then applied a snowball strategy to iteratively review notes for additional terms until saturation was reached. To identify more target topics, we tested three keyword-based filtering strategies. The detected topics were evaluated using three widely used metrics and manually reviewed by specialists. Word frequency analysis was used to highlight the prevalent terms associated with each topic. In addition, we conducted topic variation analysis among subgroups to examine differences across age- and sex-specific demographics.
resultsWe identified 9,140 PLWHs at UF Health and collected 2.9 million clinical notes. Through the iterative keyword approach, we generated a list of 91 keywords associated with HIV-related stigma. Topic modeling on sentences containing at least one keyword uncovered a wide range of topic themes associated with HIV-related stigma, social, and related behaviors circumstances, including "Mental Health Concern and Stigma", "Social Support and Engagement", "Limited Healthcare Access and Severe Illness", "Missed Appointments and HIV Care Monitoring", "Treatment Refusal and Isolation", "Intimate Partner Violence and Relationship Concerns", "Fear of Falling and Physical Health Concerns", "Substance Abuse", and "Food Insecurity and Resource Scarcity". Topic variation analysis across sex and age subgroups revealed no substantial difference between males and females; however, there were differences were observed among different ages. For example, "Fear of Falling and Physical Health Concerns" was notably more prevalent among older adults.
conclusionExtracting and understanding the HIV-related stigma and associated social and behavioral circumstances from EHR clinical notes enables scalable, time-efficient assessment and overcoming the limitations of traditional questionnaires. Findings from this research provide actionable insights to inform patient care and interventions to improve HIV-care outcomes.
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