Research on Automatic Classification and Prognosis Prediction of Intracerebral Hemorrhage Based on Deep Learning Models

Intracerebral hemorrhage (ICH) poses significant challenges in clinical management due to its high morbidity and mortalityrates. Traditional prognostic models often rely on manual assessment and conventional statistical methods, which maylack the precision and adaptability required for individualized patient care. These approaches are limited in their ability tointegrate complex, high-dimensional data, leading to suboptimal predictive performance. To address these shortcomings, wepropose a novel deep learning framework that leverages advanced computational techniques to enhance the accuracy of ICHclassification and outcome prediction. Our methodology encompasses three core components: a symbolic representation ofanatomical regions to capture the spatial dynamics of hemorrhage propagation; structured neural modules embedded withgeometric priors derived from neuroanatomy to model the interplay between lesion topography and functional impact; anda dynamic factorization technique that decouples observed clinical scores from underlying vascular dynamics, thereby disentangling confounding vascular events from observable patient trajectories. This integrative approach enables fine-grainedtemporal reasoning and facilitates the incorporation of heterogeneous data modalities, including imaging, vital signs, andclinical notes. Experimental results model outperforms existing prognostic tools, providing more accurate and interpretablepredictions of patient outcomes. By embedding domain-specific constraints and causality-inspired latent-variable formulations,our framework offers a robust and scalable solution for personalized ICH management, aligning with the interdisciplinaryfocus of computational sciences in advancing healthcare technologies.