Predicting immune reconstitution after antiretroviral therapy in HIV/AIDS using ensemble machine learning: a real-world study

Background: T-cell recovery trajectories in people living with HIV on antiretroviral therapy (ART) is a crucial unmet need for personalized monitoring and treatment optimization. Traditional statistical models have limited ability to capture the complex, non-linear relationships inherent in longitudinal clinical data. Methods: counts to prevent data leakage. Results: predictions compared to a baseline Robust Transformer model. The model accurately replicated the statistical distributions of observed outcomes and demonstrated stable learning dynamics without overfitting. 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.