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Consequently, the challenge lies not just in accessing data, but in efficiently transforming these messy, disparate sources into reliable, enduring AI solutions.This presentation outlines a comprehensive strategy to overcome these limitations and derive robust clinical insights from imperfect data. We will discuss how representation learning can address data sparsity and fragmentation by extracting stable latent features from incomplete patient histories. To tackle system heterogeneity and ensure model longevity, we introduce robust transfer learning frameworks designed to immunize algorithms against distributional shifts. Furthermore, we demonstrate how leveraging knowledge networks can bridge gaps in fragmented data by grounding models in broader biomedical context. Complementing these structural approaches, we touch upon the use of Large Language Models (LLMs) to identify clinical outcomes not directly available in structured fields, solving the problem of unobserved endpoints. 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