The article discusses the importance of joint analysis of structured and unstructured data in Electronic Health Records (EHR). It introduces a novel multimodal feature embedding generative model and contrastive loss for improved EHR feature representation. The theoretical analysis demonstrates the effectiveness of multimodal learning compared to single-modality approaches, with a focus on privacy-preserving algorithms. Simulation studies validate the proposed algorithm's performance under various configurations and its clinical utility in real-world EHR data.
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arxiv.org
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