RiskSqueezeNet: Squeeze-Excitation withAggregated Attention for Tumor Care RiskForecasting

In the continuously evolving landscape of computational medicine and intelligent health informatics, the capacity to accuratelyanticipate and predict dynamic clinical risks has become a cornerstone for enabling timely interventions and delivering trulypersonalized healthcare. Such predictive capability plays a pivotal role in supporting clinicians as they navigate complexpatient trajectories and make critical decisions under uncertainty. However, conventional machine learning and deep learningmodels often fall short in this context due to their limited ability to model intricate temporal dependencies and capture subtleyet significant clinical variations. These limitations hinder their generalizability and forecasting robustness, especially whenapplied to real-world, noisy, and irregularly sampled health data. To address these ongoing challenges, we propose anew and comprehensive modeling framework that adheres to the principles of computational intelligence. This frameworkintegrates advanced channel recalibration techniques and hierarchical attention aggregation mechanisms to improve themodel’s performance. Our approach is built around a lightweight yet expressive neural architecture that seamlessly integratessqueeze-and-excitation modules with both temporal and multi-level attention strategies. This configuration enables moreeffective signal extraction and feature discrimination from high-dimensional, multivariate clinical time series. Our designembeds a domain-sensitive forecasting scheme that combines gradient-informed temporal dynamics, adaptive multi-scaledecoding, and outcome-calibrated correction strategies. These innovations ensure the model maintains high predictive fidelityeven in the presence of sparse annotations, missing values, and irregular time intervals. The proposed framework not onlysignificantly boosts risk estimation accuracy across varying prediction horizons but also enhances model interpretability andalignment with real-world clinical workflows. Empirical tests across various datasets consistently highlight the effectivenessof our method, surpassing current state-of-the-art baselines. These results emphasize its potential to facilitate reliable,transparent, and adaptive risk assessment within contemporary clinical decision-support systems.