A two-method framework for aligning medical terminologies

The healthcare data interoperability relies on aligning disparate terminologies to a unified ontology, such as SNOMED CT. In this work, we present a framework consisting of two methods that leverages expert-curated reference sets for direct 1-to-1 mappings; augmented by algorithmic strategies for incomplete or 1-to-M cases. Firstly, for unmapped procedure (CPT) and medication (NDC) codes, we power logistic regression-based imputation and non-contextual ranking with medBERT text embeddings. Second, for aligning ambiguous diagnoses (ICD-9) codes, a patient-context-aware ranking exploits SNOMED CT’s hierarchical structure via three proximity metrics: textual cosine similarity, exact shortest-path distances, and Node2Vec embeddings. Text-based imputation yields high AUCs (0.95+ for NDC, 0.85 for CPT); and contextual ranking with Node2Vec for ICD-9’s generic mappings achieve Hits@1 of 0.37-0.45 and Hits@5 of 0.82-0.88 on curated EHR-like tests, outperforming text-only methods while delivering a significant cost reduction over computing exact distances. Interpretable confidences (K<inf>i</inf>) are calculated to reward context-specific outliers, enabling robust entity resolution in graph-triple pipelines. With this framework, we bridge gaps between medical terminologies, reducing mapping ambiguity and increasing interoperability. By supporting expert-curated reference sets with algorithmic and statistical methods, our framework advances scalable semantic integration within the healthcare domain.