A GCN-based approach to uncover misaligned synonymous terms in the UMLS Metathesaurus.

Xubing Hao, Rashmie Abeysinghe, Jay Shi, Licong Cui

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Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Xubing HaoMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.

Rashmie AbeysingheDepartment of Neurology, The University of Texas Health Science Center at Houston, Houston, TX.

Jay ShiIntermountain Healthcare, Denver, CO.

Licong CuiMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.

Funding

An informatics framework for SUDEP Risk Marker Identification and Risk AssessmentR01NS116287 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CUI, LICONG · 2020 to 2024

$1.7M

Biomedical Terminology Quality Assurance for Enhancing Clinical Queries over Electronic Health RecordsR01LM013335 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CUI, LICONG · 2020 to 2021

$663k

NINDS NIH HHS R01 NS116287NLM NIH HHS R01 LM013335

6 · The paper itself

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.

Indexed as

Unified Medical Language SystemHumansReproducibility of Results

Identifiers

PMID38222357

PMCPMC10785861

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.