ArticleJournal of the American Medical Informatics Association : JAMIA2020
Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- 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
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- Medical terminology-based computing system: a lightweight post-processing solution for out-of-vocabulary multi-word terms.Frontiers in molecular biosciences · 2022Article
- Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study.JMIR medical informatics · 2021Article
- Biomedical Vocabulary Alignment at Scale in the UMLS Metathesaurus.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2021Article
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Abstract
objectiveThe study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts. MATERIALS AND
methodsConcept sentence embeddings were generated for UMLS concepts by applying the word embedding models BioWordVec and various flavors of BERT to concept sentences formed by concatenating UMLS terms. Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS hierarchical relations. Semantic relatedness was measured by the cosine between the concepts' embedding vectors. Performance was compared with 2 traditional path-based (shortest path and Leacock-Chodorow) measurements and the publicly available concept embeddings, cui2vec, generated from large biomedical corpora. The concept sentence embeddings were also evaluated on a word sense disambiguation (WSD) task. Reference standards used included the semantic relatedness and semantic similarity datasets from the University of Minnesota, concept pairs generated from the Standardized MedDRA Queries and the MeSH (Medical Subject Headings) WSD corpus.
resultsSentence embeddings generated by BioWordVec outperformed all other methods used individually in semantic relatedness measurements. Graph convolutional network graph embedding uniformly outperformed path-based measurements and was better than some word embeddings for the Standardized MedDRA Queries dataset. When used together, combined word and graph embedding achieved the best performance in all datasets. For WSD, the enhanced versions of BERT outperformed BioWordVec.
conclusionsWord and graph embedding techniques can be used to harness terms and relations in the UMLS to measure semantic relatedness between concepts. Concept sentence embedding outperforms path-based measurements and cui2vec, and can be further enhanced by combining with graph embedding.
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