Channel Attention-driven Transformer with TemporalEmbedding for Oncology Care Risk Prediction

In alignment in Computer Science’s mission to advance both foundational theories and real-world applications—particularly those that support interdisciplinary innovation and Sustainable Development Goal 9 (Industry, Innovation and Infrastructure)—this study presents a novel Channel Attention-driven Transformer architecture enhanced with temporal embeddings for predicting oncology care risk. The motivation stems from a pressing need to improve early clinical decision-making in cancer management, where traditional risk prediction models typically rely on static features or shallow temporal mechanisms. Such approaches are often insufficient for capturing the complex interdependencies among diverse physiological signals, treatment sequences, and disease trajectories in oncology patients. To overcome these challenges, our proposed framework incorporates a channel attention mechanism that adaptively reweights multimodal feature channels based on their relevance, allowing the model to selectively emphasize clinically informative signals. In parallel, we integrate temporal embeddings—both sinusoidal and learnable—to encode short-term fluctuations and long-range temporal patterns, which are crucial for modeling disease evolution. These components are embedded within multi-head self-attention Transformer blocks, further enhanced with channel-based gating to modulate signal flow at each layer. The method was rigorously evaluated on multiple real-world oncology datasets, covering diverse cancer types and risk indicators. Empirical results demonstrate that our model consistently outperforms state-of-the-art baselines across metrics including AUC, F1-score, and early warning lead time. Notably, the architecture maintains model interpretability through attention heatmaps that offer actionable insights to clinicians. This comprehensive design—blending technical novelty, clinical relevance, and transparency—illustrates how our approach contributes to impactful computational innovations in digital health, aligning seamlessly with the journal’s interdisciplinary and socially driven scope.