Benchmarking Transformer Embedding Models for Biomedical Terminology Standardization.

ABSTRACT: Biomedical text in public databases often exhibits unstandardized terminology and inconsistencies that impede machine learning applications and hinder data integration across biomedical databases. Leveraging generalized and specialized transformer/large language models (LLMs) offers a potential scalable solution for terminology standardization. We evaluated this opportunity using the National Institutes of Health Clinical Trials Registry (CTR), which contains heterogeneous, free-text records of disease from therapeutic trials. To systematically assess the ability of machine learning methods to assign biomedical terms accurately, we benchmarked 36 approaches using transformer/LLM-based text embeddings, along with traditional text-matching algorithms, against a clinical gold standard: the Wo

SUBMITTER: Lahiri A 

PROVIDER: S-EPMC12288841 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature