Machine learning to predict retention and viral suppression in South African HIV treatment cohorts

@inproceedings{Maskew2021MachineLT,
  title={Machine learning to predict retention and viral suppression in South African HIV treatment cohorts},
  author={M. Maskew and K. Sharpey-Schafer and L. De Voux and J. Bor and M. Rennick and T. Crompton and P. Majuba and I. Sanne and P. Pisa and J. Miot},
  booktitle={medRxiv},
  year={2021}
}
Background: To optimize South Africa's HIV response and reach PEPFAR's 95:95:95 targets require same day initiations and patients successfully remaining on antiretroviral therapy, and remaining virally suppressed. Much effort and resources with HIV programmes have centered on tracking and tracing for those lost to follow-up (LTFU), through various back to care strategies to ensure retention of patients. However, programmes have noted the need for targeted, data driven and predictive approaches… Expand

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