Discrete Neural Interlingua for Ontology-Agnostic EHR Modeling

EHR foundation models create new opportunities for scalable clinical prediction across health systems, yet their deployment remains constrained by a basic tokenization bottleneck. The same clinical concept can appear as different ontology codes, local identifiers, synonyms, or free-text descriptions, while standard EHR tokenizers treat these variants as unrelated symbols. We introduce MEDIATOR, a discrete ontology-agnostic medical concept tokenizer that turns EHR harmonization into an open-form tokenization problem. Instead of relying on manual mappings, a fixed ontology vocabulary, or continuous event embeddings, MEDIATOR maps any clinical concept expressed as text into a shared vector-quantized codebook. MEDIATOR is trained with synonym supervision from paired concept descriptions, and further guided by enriched concept descriptions, empirical code co-occurrence, and curated relation graphs so that equivalent expressions collapse while non-equivalent concepts remain organized in a medically meaningful discrete space. The resulting vocabulary is compact, inspectable, reusable by standard EHR Transformers, and open to previously unseen code descriptions or text strings. Used as the token interface of an EHR Transformer, MEDIATOR improves mean AUPRC on 20 MIMIC-III tasks by 4.0% and improves zero-shot transfer to 14 eICU tasks by 8.8%, while supporting transferable attribution across hospitals.