Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania.

ABSTRACT: 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, these methods have not been widely applied to medical record data in low- and middle-income countries. We used an ensemble decision tree approach to predict risk of disengagement from HIV care (missing an appointment by ≥28 days) in Tanzania. Our approach used routine electronic medical records (EMR) from the time of antiretroviral therapy (ART) initiation through 24 months of follow-up for 178 adults (63% female). We compared prediction accuracy when using EMR-based predictors alone and in combination with sociodemographic survey data collected by a research study. Models that included only EMR-based indicator

SUBMITTER: Fahey CA 

PROVIDER: S-EPMC10021592 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature