SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation
Biomedical language models (LMs) provide strong representations for clinical tasks, but they often fail to capture the hierarchical structure among medical concepts. This limitation is especially pronounced for rare and fine-grained clinical codes in long-tailed vocabularies, where hierarchy-agnostic LM representations become less informative as concept frequency decreases. We introduce Semantic Medical Identifier (SMI), a training-free framework for constructing ontology-grounded Semantic IDs and hierarchy-aware medical concept representations. For each diagnosis, procedure, or medication concept, SMI defines its Semantic ID as the coarse-to-fine path through an expert-defined medical on...
| Authors | Ziyang Song, Lia Shen, Yixuan Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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