<ul data-eligibleForWebStory="true"><li>Research objective: Characterize stigma dimensions, social, and related behavioral circumstances in PLWHs' clinical notes using natural language processing.</li><li>Methodology: Utilized a cohort of 9,140 PLWHs, applied Latent Dirichlet Allocation for topic modeling analysis on EHR notes.</li><li>Methodology (contd.): Domain experts created a stigma keyword list, iteratively reviewed notes, and conducted word frequency analysis.</li><li>Findings: Uncovered various themes like 'Mental Health Concern and Stigma', 'Social Support', 'Limited Healthcare Access', 'Treatment Refusal', etc.</li><li>Conclusion: Topic modeling identified stigma and social themes, aiding in scalable assessment and enhancing patient outcomes.</li></ul>

A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV

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