Improving clinical data standardization in surgical notes through biomedical entity linking with contextual evidence from large language models

Surgical notes contain essential clinical information for postoperative care, yet free-text format and institutional variability limit their use for standardized data representation and secondary analysis. Biomedical entity linking enables mapping of heterogeneous clinical expressions to standardized ontologies such as SNOMED-CT, supporting semantic interoperability. However, existing approaches often rely on predefined mention spans through named entity recognition (NER), which is labor-intensive and may introduce errors. We analyzed 9,051 gastric cancer surgical notes from Seoul National University Hospital. We developed a framework that leverages an open-source large language model (LLM; LLaMA-3.1-8B) to identify contextually relevant text segments, termed evidence spans, which provide cues for ontology-based entity linking. These spans were explicitly marked and used to fine-tune SapBERT, a pretrained embedding-based biomedical encoder. We compared multiple input variants against conventional pipelines and LLM-based approaches, including in-context learning and re-ranking. Incorporating evidence spans improved entity linking performance across metrics, with gains of +2.7 in Recall@1 and +2.2 in mean Average Precision at 3 (mAP@3) compared to raw text inputs. Evidence-guided models outperformed other LLM-based approaches, with additional gains when using the evidence marker token as the pooled representation. Attention analysis indicated that explicit evidence span marking reinforced the model’s focus on ontology-relevant context while reducing attention to irrelevant text. Leveraging LLM-derived contextual evidence improves ontology-based representation of clinical text by enhancing biomedical entity linking. This approach provides a practical strategy for standardizing unstructured surgical notes and supports more reliable secondary use of clinical data in real-world healthcare settings. More broadly, the framework supports mapping of unstructured clinical text to standardized ontologies, contributing to semantic interoperability and enabling downstream secondary use of clinical data.