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The current UMLS (Unified Medical Language System) Metathesaurus constructionprocess for integrating over 200 biomedical source vocabularies is expensiveand error-prone as it relies on the lexical algorithms and human editors fordeciding if the two biomedical terms are synonymous. Recent advances in NaturalLanguage Processing such as Transformer models like BERT and its biomedicalvariants with contextualized word embeddings have achieved state-of-the-art(SOTA) performance on downstream tasks. We aim to validate if these approachesusing the BERT models can actually outperform the existing approaches forpredicting synonymy in the UMLS Metathesaurus. In the existing Siamese Networkswith LSTM and BioWordVec embeddings, we replace the BioWordVec embeddings withthe biomedical BERT embeddings extracted from each BERT model using differentways of extraction. In the Transformer architecture, we evaluate the use of thedifferent biomedical BERT models that have been pre-trained using differentdatasets and tasks. Given the SOTA performance of these BERT models for otherdownstream tasks, our experiments yield surprisingly interesting results: (1)in both model architectures, the approaches employing these biomedicalBERT-based models do not outperform the existing approaches using SiameseNetwork with BioWordVec embeddings for the UMLS synonymy prediction task, (2)the original BioBERT large model that has not been pre-trained with the UMLSoutperforms the SapBERT models that have been pre-trained with the UMLS, and(3) using the Siamese Networks yields better performance for synonymyprediction when compared to using the biomedical BERT models.

Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus