ArticleFrontiers in artificial intelligence2026
Predicting HIV viral non-suppression in Uganda: development and validation of machine learning and risk stratification models using routine EMR data.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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12 authors.
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Abstract
Background: Viral non-suppression is the primary actionable risk state in routine HIV care, yet most individuals are identified after virological failure and/or drug resistance, rather than proactively. In Uganda and similar resource-limited settings, routine electronic medical records (EMR) are collected at scale but remain underused for targeted, data-enabled risk stratification. We aimed to develop and internally validate machine learning and regularized regression models for predicting viral non-suppression using routine monitoring data. Methods: We developed and internally validated prediction models for viral non-suppression (viral load ≥1,000 copies/mL) using routinely recorded EMR variables from the TASO Uganda open cohort (2014-2024; Results: On the test set ( Conclusions: Routine EMR data support meaningful, calibrated viral non-suppression risk prediction across a large, multi-site Ugandan HIV program. At a capacity-first threshold, both models achieved approximately five-fold enrichment over background prevalence, with clinical utility confirmed by decision curve analysis. Prospective external validation and workflow integration are required before deployment.
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