ArticleFrontiers in immunology2026
Predicting immune reconstitution after antiretroviral therapy in HIV/AIDS using ensemble machine learning: a real-world study.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Accurate prediction of long-term CD4 Methods: We developed a heterogeneous stacking ensemble framework to predict longitudinal CD4 Results: On an independent test set (n=1,088), the ensemble achieved an R Conclusion: Our ensemble learning framework provides a robust and clinically applicable tool for forecasting multi-dimensional immune reconstitution in HIV care. By synthesizing diverse algorithmic perspectives without relying on baseline immunology, it offers a foundation for data-driven clinical decision support to personalize long-term treatment monitoring.
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