A GCN-Based Approach To Uncover Misaligned Synonymous Terms In The Umls Metathesaurus

Authors

Language

English

Publication Date

1-1-2023

Journal

AMIA Summits on Translational Science Proceedings

PMID

38222357

PMCID

PMC10785861

PubMedCentral® Posted Date

January 2024

PubMedCentral® Full Text Version

Post-print

Abstract

The Unified Medical Language System (UMLS), a large repository of biomedical vocabularies, has been used for supporting various biomedical applications. Ensuring the quality of the UMLS is critical to maintain both the accuracy of its content and the reliability of downstream applications. In this work, we present a Graph Convolutional Network (GCN)-based approach to identify misaligned synonymous terms organized under different UMLS concepts. We used synonymous terms grouped under the same concept as positive samples and top lexically similar terms as negative samples to train the GCN model. We applied the model to a test set and suggested those negative samples predicted to be synonymous as potentially misaligned synonymous terms. A total of 147,625 suggestions were made. A human expert evaluated 100 randomly selected suggestions and agreed with 60 of them. The results indicate that our GCN-based approach shows promise to help improve the synonymy grouping in the UMLS.

Keywords

Humans, Unified Medical Language System, Reproducibility of Results

Published Open-Access

yes

Recommended Citation

, "A GCN-Based Approach To Uncover Misaligned Synonymous Terms In The Umls Metathesaurus" (2023). Faculty, Staff and Student Publications. 137.
https://digitalcommons.library.tmc.edu/uthshis_docs/137