Faculty, Staff and Student Publications

Language

English

Publication Date

4-1-2026

Journal

International Journal of Medical Informatics

DOI

10.1016/j.ijmedinf.2026.106269

PMID

41518824

PMCID

PMC13177315

PubMedCentral® Posted Date

5-16-2026

PubMedCentral® Full Text Version

Author MSS

Abstract

Objective: To 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.

Methods: We 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.

Results: We 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.

Conclusion: Extracting 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.

Keywords

Humans, HIV Infections, Electronic Health Records, Social Stigma, Female, Male, Adult, Middle Aged, Natural Language Processing, Human Immunodeficiency Virus, Stigma, Topic Modeling, Natural Language Processing, Electronic Health Records, Clinical Notes

Published Open-Access

yes

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