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Lluna improves multimodal predictions when some input modalities are missing by combining private unimodal predictions with a graph-based residual collaboration branch and progressive consistency training, achieving a 4.26% average improvement in Mean-Missing over baselines across eight datasets.
The paper proposes a multimodal prompt-learning framework to handle missing modalities in electronic health records for robust clinical prediction in intensive care units, introducing four prompt types to capture dependencies and interactions.