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INTRODUCTION Real-time electronic adherence monitoring (EAM) systems could inform on-going risk assessment for HIV viraemia and be used to personalize viral load testing schedules. We evaluated the potential of real-time EAM (transferred via cellular signal) and standard EAM (downloaded via USB cable) in rural Uganda to inform individually differentiated viral load testing strategies by applying machine learning approaches. METHODS We evaluated an observational cohort of persons living with HIV and treated with antiretroviral therapy (ART)
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