Maintaining retention in care (RIC) for people living with HIV (PLWH) helps achieve viral suppression and reduce onward transmission. This study aims to identify the best machine learning model that predicts the RIC transition over time. Extracting f...
Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients w...
The UNAIDS 90-90-90 target has prioritized achieving high rates of viral suppression. We identified factors associated with viral suppression among HIV-positive gay, bisexual, and other men who have sex with men (GBMSM) in Kisumu, Kenya. HIV-positive...