Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model.

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 risk populations for suboptimal HIV service utilisation, they are limited in terms of explaining relationships between predictors. To further understand these relationships, we implemented machine learning methods optimised for predictive power and traditional statistical methods.

Validation and Improvement of a Machine Learning Model to Predict Interruptions in Antiretroviral Treatment in South Africa.

Introduction: Machine learning algorithms are increasingly being used to inform HIV prevention and detection strategies. We validated and extended a previously developed machine learning model for patient retention on antiretroviral therapy in a new geographic catchment area in South Africa.

Methods: We compared the ability of an adaptive boosting algorithm to predict interruption in treatment (IIT) in 2 South African cohorts from the Free State and Mpumalanga and Gauteng and North West (GA/NW) provinces.

Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts.

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 anonymised, patient-level HIV programmatic data from two districts in South Africa, 2016-2018. We developed patient risk scores for two outcomes: (1) visit attendance ≤ 28 days of the next scheduled clinic visit and (2) suppression of the next HIV viral load (VL).