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This paper proposes a structural variant of the Factorial Hidden Markov Model to analyze and interpret disease trajectories in Type 2 diabetes patients using electronic health records, revealing clinically meaningful patterns and progression pathways.
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.