Unified Medical Language System resources improve sieve-based generation and Bidirectional Encoder Representations from Transformers (BERT)–based ranking for concept normalization
Journal of the American Medical Informatics Association2020Vol. 27(10), pp. 1510–1519
Citations Over TimeTop 18% of 2020 papers
Abstract
Our generate-and-rank framework for UMLS concept normalization integrates key UMLS features like preferred terms and semantic types with a neural network-based ranking model to accurately link phrases in text to UMLS concepts.
Related Papers
- → Mapping the Gene Ontology into the Unified Medical Language System(2004)45 cited
- → Thesaurus-based query and document expansion in conceptual indexing with UMLS: Application in medical information retrieval(2007)12 cited
- Mapping medical vocabularies to the Unified Medical Language System.(1996)
- → Combining MeSH Thesaurus with UMLS in pseudo relevance feedback to improve biomedical information retrieval(2016)2 cited
- Mapping the Gene Ontology into the Unified Medical Language System: Research Papers(2004)