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This paper introduces ICD-Deepresearch, a system that combines EHR foundation models with language models and medical search to forecast future ICD codes for patient visits, achieving improved accuracy and physician-rated usefulness over baseline methods.
This paper introduces xMICD, a method that combines ICD code groupings with pre-trained embedding similarities to create low-dimensional, clinically interpretable patient representations, achieving predictive performance comparable to embedding-based approaches.
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.