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

ABSTRACT: 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). Demographic, clinical, behavioral and laboratory data were investigated in multiple models as predictor variables of attending the next scheduled visit and VL results at the next test. Three classification algorithms (logistical regression, random forest and AdaBoost) were evaluated for building predictive models. Data were randomly sampled on a 70/30 split int

SUBMITTER: Maskew M 

PROVIDER: S-EPMC9325703 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature