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

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

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...

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Bibliographic Details
Main Authors: Williams Kwarah, Frances Baaba da-Costa Vroom, Duah Dwomoh, Samuel Bosomprah
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Global and Public Health
Subjects:
HIV treatment interruption
Machine learning
Predictive modeling
Online Access:https://doi.org/10.1186/s44263-025-00184-4
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