A Hybrid Deep Learning Approach Using Temporal Attention Networks and Gradient-Boosted Ensemble Methods

Abstract

The integration of artificial intelligence in healthcare has revolutionized predictive analytics, enabling more accurate patient outcome predictions and personalized treatment recommendations. This paper presents a novel hybrid deep learning framework that combines Temporal Attention Networks (TAN) with Gradient-Boosted Ensemble Methods (GBEM) for enhanced healthcare outcome prediction. Our proposed methodology addresses critical challenges in healthcare analytics, including handling multivariate temporal data, managing missing values, and providing interpretable predictions. We introduce a dual-pathway architecture where TAN processes sequential patient data through self-attention mechanisms while GBEM handles static clinical features through iterative gradient optimization. The framework incorporates a novel cross-modal fusion module that dynamically weights contributions from both pathways based on data characteristics. Extensive experiments on three benchmark healthcare datasets demonstrate that our approach achieves state-of-the-art performance with 94.7% accuracy, 92.3% sensitivity, and 96.1% specificity in patient outcome prediction, outperforming existing methods by 4.2-8.7%. Deep parametric analysis reveals optimal hyperparameter configurations and elucidates the relationship between model complexity and predictive performance. The proposed framework offers significant implications for clinical decision support systems and personalized medicine applications.