Predicting HIV viral non-suppression in Uganda: development and validation of machine learning and risk stratification models using routine EMR data

BackgroundViral 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 colle...

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Autori principali:	Maria Magdalene Namaganda, Stathis Gennatas, Laura Merson, Esteban Garcia, Tom Edinburgh, Joyce Nakatumba Nabende, David Patrick Kateete, Charles Batte, Misaki Wayengera, Daudi Jjingo, Edgar Kigozi, Gerald Mboowa
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Frontiers Media S.A. 2026-07-01
Serie:	Frontiers in Artificial Intelligence
Soggetti:	antiretroviral therapy decision curve analysis electronic medical records explainable artificial intelligence HIV viral non-suppression machine learning
Accesso online:	https://www.frontiersin.org/articles/10.3389/frai.2026.1869992/full
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