Applying Machine Learning to Predict Loss to Follow-Up Among People Living With HIV in Haiti Using a National Electronic Medical Record Cohort

ObjectivesLoss to follow-up (LTFU) among people living with HIV (PLHIV) remains a major barrier to epidemic control. This study developed machine learning (ML) models to forecast individual risk of LTFU using routine electronic medical record (EMR) data.MethodsWe analyzed data from Haiti’s national...

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Główni autorzy:	Jiaqin Wu, Bryan Shaw, Babatunji Oni, Kurt Jean-Charles, Darwin Dorestan, Marie Bien-Aime, Daphne Compere-Louis, Venise Dorce, Vladimy Jean-Pierre, Marc Elie Joseph, Nancy Rachel Labbe
Format:	Artigo
Język:	Inglês
Wydane:	Frontiers Media S.A. 2026-04-01
Seria:	International Journal of Public Health
Hasła przedmiotowe:	electronic medical records Haiti HIV loss to follow-up machine learning antiretroviral therapy
Dostęp online:	https://www.ssph-journal.org/articles/10.3389/ijph.2026.1609496/full
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