Generalizable and scalable multistage biomedical concept normalization leveraging large language models.

ABSTRACT:

Background

Biomedical entity normalization is critical to biomedical research because the richness of free-text clinical data, such as progress notes, can often be fully leveraged only after translating words and phrases into structured and coded representations suitable for analysis. Large Language Models (LLMs), in turn, have shown great potential and high performance in a variety of natural language processing (NLP) tasks, but their application for normalization remains understudied.

Methods

We applied both proprietary and open-source LLMs in combination with several rule-based normalization systems commonly used in biomedical research. We used a two-step LLM integration approach, (1) using an LLM to generate alternative phrasings of a source utterance, and (2) to prune c

SUBMITTER: Dobbins NJ 

PROVIDER: S-EPMC12527512 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature