Robust Prototype-Driven Patient Representation Enhancement for Disease Prediction

Electronic health records (EHR) contain sequential patient visit data with critical features for disease prediction. Recent inter-patient modeling approaches leverage information from other patients to improve generalization, but they often face challenges like noisy predictions due to ambiguous latent spaces and neglect intra-class diversity. To address these issues, we propose RPPRE, a Robust Prototype-driven Patient Representation Enhancer for EHR-based disease prediction. RPPRE enhances both inter-class discrimination and intra-class diversity by first stratifying disease classes into core, intermediate, and peripheral prototypes, then using a contrastive loss to align patient representations with these prototypes. Experiments on the MIMIC-III Respiratory and PhysioNet Sepsis datasets show that RPPRE consistently improves AUROC, AUPRC, and F1-score across seven backbone models and outperforms existing inter-patient approaches. Ablation studies further validate the importance of prototype stratification and contrastive enhancement. Our code is released at https://github.com/cp3mvp-24/RPPRE.