Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.

Journal: AIDS (London, England)
Published Date: Feb 24, 2025

Abstract

OBJECTIVES: Achieving optimal adherence to antiretroviral therapy (ART) and viral suppression is still insufficient for attaining the UNAIDS 95-95-95 target of 2030, especially among adolescents with HIV (AWHIV). This study sought to develop a model to predict poor adherence risk among AWHIV and identify associated risk factors.

Authors

  • Claire Najjuuko
    Division of Computational and Data Sciences.
  • Rachel Brathwaite
    Brown School, Washington University in St. Louis, MO, USA.
  • Ziqi Xu
    Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis , Missouri , USA.
  • Samuel Kizito
    Brown School, Washington University in St. Louis, MO, USA.
  • Chenyang Lu
    Department of Computer Science and Engineering, Washington University, St. Louis, MO.
  • Fred M Ssewamala
    Brown School, Washington University in St. Louis, MO, USA.

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