ArticleStudies in health technology and informatics2022
Adding an Attention Layer Improves the Performance of a Neural Network Architecture for Synonymy Prediction in the UMLS Metathesaurus.
Article in Studies in health technology and informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Nested CNN architecture for three-dimensional dose distribution prediction in tomotherapy for prostate cancer.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- 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
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Abstract
backgroundTerminology integration at the scale of the UMLS Metathesaurus (i.e., over 200 source vocabularies) remains challenging despite recent advances in ontology alignment techniques based on neural networks.
objectivesTo improve the performance of the neural network architecture we developed for predicting synonymy between terms in the UMLS Metathesaurus, specifically through the addition of an attention layer.
methodsWe modify our original Siamese neural network architecture with Long-Short Term Memory (LSTM) and create two variants by (1) adding an attention layer on top of the existing LSTM, and (2) replacing the existing LSTM layer by an attention layer.
resultsAdding an attention layer to the LSTM layer resulted in increasing precision to 92.38% (+3.63%) and F1 score to 91,74% (+1.13%), with limited impact on recall at 91.12% (-1.42%).
conclusionsAlthough limited, this increase in precision substantially reduces the false positive rate and minimizes the need for manual curation.
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