Named entity recognition in chinese electronic medical records based on large language models

Named Entity Recognition, as a core task in Natural Language Processing, plays a crucial role in identifying medical entities such as diseases and symptoms in Electronic Medical Records, which is of great significance for clinical decision support and the construction of medical knowledge bases. However, traditional methods rely heavily on large amounts of annotated data and complex models, resulting in high training and inference costs. This paper proposes a generative medical NER method that integrates semantic retrieval and prompt learning with large language models. First, a sentence-level vector database is constructed to semantically encode EMRs for retrievable representations. Then, based on the input sentence, semantic similarity retrieval is performed, and similar examples are dynamically injected into a prompt template to guide the model in entity extraction. Finally, entity type annotation results are generated through structured special markers, enabling direct decoding output. Experimental results demonstrate that the proposed method performs well on both a self-constructed EMR dataset and the Ruijin Hospital diabetes dataset, and exhibits strong robustness and transferability, especially in low-resource scenarios.

Key Words: named entity recognition; electronic medical records; large language models