CDE-Mapper combines query decomposition, ensemble retrieval, knowledge filtering, and LLM reranking, and reports top-1 accuracy gains over SapBERT, KRISS BERT, BioBERT-snomed, and PromptLink on clinical concept linking benchmarks.
SNOBERT: A Benchmark for clinical notes entity linking in the SNOMED CT clinical terminology
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abstract
The extraction and analysis of insights from medical data, primarily stored in free-text formats by healthcare workers, presents significant challenges due to its unstructured nature. Medical coding, a crucial process in healthcare, remains minimally automated due to the complexity of medical ontologies and restricted access to medical texts for training Natural Language Processing models. In this paper, we proposed a method, "SNOBERT," of linking text spans in clinical notes to specific concepts in the SNOMED CT using BERT-based models. The method consists of two stages: candidate selection and candidate matching. The models were trained on one of the largest publicly available dataset of labeled clinical notes. SNOBERT outperforms other classical methods based on deep learning, as confirmed by the results of a challenge in which it was applied.
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2025 1verdicts
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CDE-Mapper: Using Retrieval-Augmented Language Models for Linking Clinical Data Elements to Controlled Vocabularies
CDE-Mapper combines query decomposition, ensemble retrieval, knowledge filtering, and LLM reranking, and reports top-1 accuracy gains over SapBERT, KRISS BERT, BioBERT-snomed, and PromptLink on clinical concept linking benchmarks.