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New EHR models leverage ICD code hierarchy for improved predictions

Researchers have developed new methods for electronic health record (EHR) foundation models to better utilize the hierarchical structure of ICD diagnosis codes. Current models treat these codes as flat tokens, ignoring their inherent relationships. This work explores augmenting BERT-style transformers with hierarchical tokens and incorporating hierarchy into graph-based code representations. Experiments on MIMIC-IV and eICU datasets demonstrate that explicitly encoding ICD hierarchy improves downstream prediction accuracy and cross-dataset transferability, with the most effective hierarchy level varying by task and model. AI

RANK_REASON Research paper published on arXiv detailing novel methods for EHR foundation models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New EHR models leverage ICD code hierarchy for improved predictions

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Research paper published on arXiv detailing novel methods for EHR foundation models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Megha Thukral, Dong Gyun Kang, Rudra Pratap Singh, Shruthi Kashinath Hiremath, Katrin H\"ansel, Thomas Pl\"otz ·

    Hierarchical Modeling of ICD Codes in EHR Foundation Models

    arXiv:2606.15447v1 Announce Type: new Abstract: Electronic health record foundation models typically treat ICD diagnosis codes as flat tokens, overlooking the clinically meaningful hierarchical structure that captures disease families, subcategories, and fine-grained diagnostic d…