RQCare: A Residual Quantization Model for Disease Representation and Diagnosis Prediction in Healthcare Data

Abstract

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.

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