Learning multimodal representations for incomplete EHRs with retrieval-augmented personalized modality recovery

As Electronic Health Records (EHRs) proliferate, multimodal methods become increasingly valuable for clinical prediction. However, missing modalities lead to modality imbalance and degraded fusion performance. Recovering missing modalities has emerged as an effective way to mitigate this issue. Despite this, existing recovery strategies neither efficiently use globally similar conditions as modal-unique priors nor fuse them with the patient’s own available modality, leading to the loss of patient-specific information. To fill these gaps, we propose RAMCare , a two-step missing modality recovery framework that combines r etrieval- a ugmented priors and m ixture-of-experts offset modeling for incomplete EHR representation learning, which is realized by a three-stage training process: (1) a Relevant Case Retrieval Augmented Module that builds a global case library of cross-modal aligned condition pairs via co-clustering, enabling efficient retrieval of condition-matched modal-unique priors for preliminary recovery; (2) a Mixture of Experts Offset Module that selects condition experts based on retrieved weights and patient-specific features, mapping the retrieved deviation to personalized offsets of the missing modality to capture individual variation and refine the recovery; (3) a finetuning stage that further enhances recovery and multimodal fusion performance. Evaluated on two real-world datasets with missing modalities, RAMCare achieves state-of-the-art performance.