Medical concepts, the core entities in Electronic Health Records (EHRs), provide essential inputs for clinical decision-making systems. However, most existing healthcare models still rely on massive concept-specific embedding tables, resulting in substantial memory overhead. Recent studies compress medical concepts into discrete code sequences for memory efficiency, but their flat semantic quantization fails to explicitly encode the hierarchical structure of medical ontologies, thereby limiting clinical interpretability. To this end, we propose MedRQ, an ontology-driven residual vector quantization framework that aligns discrete codes with multi-level clinical ontologies. By incorporating hierarchical supervision into the quantization process, MedRQ generates compact and ontology-consistent concept representations that generalize seamlessly across healthcare prediction tasks. Experiments on two real-world EHR datasets demonstrate that MedRQ significantly outperforms state-of-the-art baselines while reducing memory usage.

Electronic Health Records (EHRs) offer rich longitudinal data for clinical prediction, but existing deep models struggle to jointly capture semantic richness and hierarchical structure. We propose RQCare, a novel framework that integrates semantic and structural information for interpretable hierarchical disease representation learning. RQCare consists of three components: (1) a Disease Embedding Residual Quantization module that learns discrete, interpretable hierarchies from embeddings; (2) a Dual-Graph Structure Learning module that refines representations using both patient-disease interactions and ICD hierarchy; (3) a GRU-based temporal model with attention for next-visit prediction. Evaluated on MIMIC-III and MIMIC-IV, RQCare outperforms state-of-the-art baselines, achieving up to 6.06% and 2.84% gains in Precision@10, respectively.

Sparse representation has recently been extensively studied in the field of image restoration. Many sparsity-based approaches enforce sparse coding on patches with certain constraints. However, extracting structural information is a challenging task in the field image restoration. Motivated by the fact that structured sparse representation (SSR) method can capture the inner characteristics of image structures, which helps in finding sparse representations of nonlinear features or patterns, we propose the SSR approach for image restoration. Specifically, a generalized model is developed using structured restraint, namely, the group [Formula: see text]-norm of the coefficient matrix is introduced in the traditional sparse representation with respect to minimizing the differences within classes and maximizing the differences between classes for sparse representation, and its applications with image restoration are also explored. The sparse coefficients of SSR are obtained through iterative optimization approach. Experimental results have shown that the proposed SSR technique can significantly deliver the reconstructed images with high quality, which manifest the effectiveness of our approach in both peak signal-to-noise ratio performance and visual perception.