ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Adversarial Learning of Knowledge Embeddings for the Unified Medical Language System.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Representation learning to advance multi-institutional studies with electronic health record data from US and France.Nature communications · 2026Article
- Enhancing diagnostic precision for rare diseases using case-based reasoning.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Improving medical term embeddings using UMLS Metathesaurus.BMC medical informatics and decision making · 2022Article
- The impact of learning Unified Medical Language System knowledge embeddings in relation extraction from biomedical texts.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Bootstrapping Adversarial Learning of Biomedical Ontology Alignments.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
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Authors and funding
3 authors.
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Abstract
Incorporating the knowledge encoded in the Unified Medical Language System (UMLS) in deep learning methods requires learning knowledge embeddings from the knowledge graphs available in UMLS: the Metathesaurus and the Semantic Network. In this paper we present a technique using Generative Adversarial Networks (GANs) for learning UMLS embeddings and showcase their usage in a clinical prediction model. When the UMLS embeddings are available, the predictions improve by up to 6.9% absolute F
Identifiers
31259009PMC6568073What OpenQuestion holds
Registered trials
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