HIV treatment programs face challenges in identifying patients at risk for loss-to-follow-up
and uncontrolled viremia. We applied predictive machine learning algorithms to …

Retention of antiretroviral (ART) patients is a priority for achieving HIV epidemic control in
South Africa. While machine-learning methods are being increasingly utilised to identify high …

Machine learning methods for health care delivery optimization have the potential to
improve retention in HIV care, a critical target of global efforts to end the epidemic. However …

Predictive analytics can be used to identify people with HIV currently retained in care who
are at risk for future disengagement from care, allowing for prioritization of retention …

Study design: RE, JC, KSS, LDV, SL, TM, MM; Data Collection: ME; Data Analysis: RE, JC,
KSS; Data Interpretation: RE, JC, KSS, MM; Supervision: MM; Writing–original draft: RE; …

Background Although the success of HIV treatment programs depends on retention and viral
suppression, routine program monitoring of these outcomes may be incomplete. We used …

Background. Most evaluations of loss to follow-up (LTFU) in human immunodeficiency virus
(HIV) treatment programs focus on baseline predictors, prior to antiretroviral therapy (ART) …

Background Optimizing retention in human immunodeficiency virus (HIV) treatment may
require sequential behavioral interventions based on patients' response. Methods In a …

Background: A core objective of HIV/AIDS programming is keeping clients on treatment to
improve their health outcomes and to limit spread. Machine learning and artificial …

Background In generalized epidemic settings, strategies are needed to prioritize individuals
at higher risk of human immunodeficiency virus (HIV) acquisition for prevention services. We …