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This paper investigates explicit encoding of ICD-10-CM hierarchy in EHR foundation models, using hierarchical token augmentation and graph-based code representations. Experiments on MIMIC-IV and eICU show improvements over flat code representations for in-domain and cross-dataset prediction tasks.
This paper introduces a lightweight, end-to-end benchmarking framework for reproducible synthetic Electronic Health Record (EHR) generation, unifying multiple baselines (MedGAN, CorGAN, PromptEHR, HALO) and a GPT-2 baseline under a single pipeline with a rigorous privacy-utility evaluation suite.