Public articles linked to the same research event.
Studies in health technology and informatics 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.
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.