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# SNOMED CT

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Studies in health technology and informatics Sep 17, 2026
Medical Concept Normalization of German Clinical Expressions to SNOMED CT: Domain Embedding Retrieval with LLM Reranking Outperforms LLM-Only
This study investigates medical concept normalization of short German clinical expressions to SNOMED CT by comparing a direct GPT-5.4 LLM-only approach with a hybrid approach combining medBERT.de bi-encoder embedding retrieval and RAG-based LLM reranking, finding that the LLM-only baseline achieves Recall@1 of 0.235, Recall@3 of 0.297, and Recall@5 of 0.303, while embedding-based retrieval reaches Recall@1 of 0.681, Recall@3 of 0.783, and Recall@5 of 0.812, and adding RAG reranking further improves Recall@1 to 0.771 with Recall@3 and Recall@5 at 0.809 and 0.812.
This study investigates medical concept normalization of short German clinical expressions to SNOMED CT by comparing a direct GPT-5.4 LLM-only approach with a hybrid approach combining medBERT.de bi-encoder embedding retrieval and RAG-based LLM reranking, finding that the LLM-only baseline achieves Recall@1 of 0.235, Recall@3 of 0.297, and Recall@5 of 0.303, while embedding-based retrieval reaches Recall@1 of 0.681, Recall@3 of 0.783, and Recall@5 of 0.812, and adding RAG reranking further improves Recall@1 to 0.771 with Recall@3 and Recall@5 at 0.809 and 0.812.
German Research Centre for Artificial Intelligence Medical University of Graz Center For Biomarker Research In Medicine
Helena Adam（First author）
Medical Concept Normalization SNOMED CT Retrieval-Augmented Generation GPT-5.4
medRxiv Sep 13, 2026
Hybrid lexical-semantic retrieval over SNOMED CT: combining two retrieval paradigms to facilitate clinical data entry
The work proposes and implements a hybrid retrieval architecture that lets deterministic lexical matching and learned semantic matching coexist over SNOMED CT's own curated descriptions, combining multi-prefix search, BioLORD-2023-M embeddings, an optional BGE cross-encoder for re-ranking, and a local LLM for query normalization, with Reciprocal Rank Fusion and a hierarchy filter; on a search-only linking evaluation over 542 disease mentions from the DisTEMIST corpus (Spanish, zero-shot), semantic search with re-ranking and no LLM query pre-processing reached accuracy@1 of 0.60 and recall@10 of 0.80, and on 12,897 mentions from English real-EHR discharge notes a field-scoped typeahead placed the concept on the top-10 picker list for 73% of mentions (accuracy@1 0.
The work proposes and implements a hybrid retrieval architecture that lets deterministic lexical matching and learned semantic matching coexist over SNOMED CT's own curated descriptions, combining multi-prefix search, BioLORD-2023-M embeddings, an optional BGE cross-encoder for re-ranking, and a local LLM for query normalization, with Reciprocal Rank Fusion and a hierarchy filter; on a search-only linking evaluation over 542 disease mentions from the DisTEMIST corpus (Spanish, zero-shot), semantic search with re-ranking and no LLM query pre-processing reached accuracy@1 of 0.60 and recall@10 of 0.80, and on 12,897 mentions from English real-EHR discharge notes a field-scoped typeahead placed the concept on the top-10 picker list for 73% of mentions (accuracy@1 0.
Alejandro López Osornio（First author）
Hybrid concept retrieval SNOMED CT Reciprocal Rank Fusion Entity Linking

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