ArticleProceedings of the ... International World-Wide Web Conference. International WWW Conference2021
Biomedical Vocabulary Alignment at Scale in the UMLS Metathesaurus.
Article in Proceedings of the ... International World-Wide Web Conference. International WWW Conference, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Nested Named Entity Recognition using Multilayer BERT-based Model: Notebook for the BioASQ Lab at CLEF 2024.CEUR workshop proceedings. · 2024Article
- Assessing the impact of transitioning to 11th revision of the International Classification of Diseases (ICD-11) on comorbidity indices.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A GCN-based approach to uncover misaligned synonymous terms in the UMLS Metathesaurus.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Mapping Chinese Medical Entities to the Unified Medical Language System.Health data science · 2023Article
- Adding an Attention Layer Improves the Performance of a Neural Network Architecture for Synonymy Prediction in the UMLS Metathesaurus.Studies in health technology and informatics · 2022Article
- A Simple Standard for Sharing Ontological Mappings (SSSOM).Database : the journal of biological databases and curation · 2022Article
- Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2022Article
- Context-Enriched Learning Models for Aligning Biomedical Vocabularies at Scale in the UMLS Metathesaurus.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
With 214 source vocabularies, the construction and maintenance process of the UMLS (Unified Medical Language System) Metathesaurus terminology integration system is costly, time-consuming, and error-prone as it primarily relies on (1) lexical and semantic processing for suggesting groupings of synonymous terms, and (2) the expertise of UMLS editors for curating these synonymy predictions. This paper aims to improve the UMLS Metathesaurus construction process by developing a novel supervised learning approach for improving the task of suggesting synonymous pairs that can scale to the size and diversity of the UMLS source vocabularies. We evaluate this deep learning (DL) approach against a rule-based approach (RBA) that approximates the current UMLS Metathesaurus construction process. The key to the generalizability of our approach is the use of various degrees of lexical similarity in negative pairs during the training process. Our initial experiments demonstrate the strong performance across multiple datasets of our DL approach in terms of recall (91-92%), precision (88-99%), and F1 score (89-95%). Our DL approach largely outperforms the RBA method in recall (+23%), precision (+2.4%), and F1 score (+14.1%). This novel approach has great potential for improving the UMLS Metathesaurus construction process by providing better synonymy suggestions to the UMLS editors.
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Registered trials
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.