PAID: Patient-Identity Contrastive Learning from Recurrent Admissions in Multimodal EHRs

Generalizable representation learning from multimodal electronic health records (EHRs) is difficult because outcome labels are task-specific and each admission provides an incomplete view of patient state. Recurrent admissions offer outcome-independent supervision: they share patient-level context while differing in acute condition and modality availability. We propose Patient-Identity Contrastive Learning (PAID), which uses this relation in an asymmetric dual-space Transformer. A patient token (PAT) is aligned across admissions, while an admission token (ADM) retains a separate route for episode-specific evidence. Availability-masked attention, cross-modal reconstruction, and a structured matching score support learning from incomplete observations. On patient-disjoint MIMIC-IV and eICU cohorts, PAID achieves the highest AUROC on all 12 endpoints, with maximum gains of 0.0135 and 0.0247, respectively. Its prediction systems improve all 21 evaluated few-shot settings, and PAT achieves 51.4% Recall@5 for same-patient retrieval. These results support recurrent admissions as a useful source of supervision for reusable multimodal representations with task-dependent patient and admission roles.