Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review.

Journal: BMC global and public health
Published Date: Jul 24, 2025

Abstract

BACKGROUND: HIV treatment interruption remains a significant barrier to achieving global HIV/AIDS control goals. Machine learning (ML) models offer potential for predicting treatment interruption by leveraging large clinical data. Understanding how these models were developed, validated, and applied remains essential for advancing research.

Authors

  • Williams Kwarah
    Department of Biostatistics, School of Public Health, University of Ghana, Accra, Ghana. Kwarah@gmail.com.
  • Frances Baaba da-Costa Vroom
    Department of Biostatistics, School of Public Health, University of Ghana, Accra, Ghana.
  • Duah Dwomoh
    Department of Biostatistics, School of Public Health, University of Ghana, Accra, Ghana.
  • Samuel Bosomprah
    Department of Biostatistics, School of Public Health, University of Ghana, Accra, Ghana.

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