 Unified Medical Language System
"Unified Medical Language System" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
A research and development program initiated by the NATIONAL LIBRARY OF MEDICINE to build knowledge sources for the purpose of aiding the development of systems that help health professionals retrieve and integrate biomedical information. The knowledge sources can be used to link disparate information systems to overcome retrieval problems caused by differences in terminology and the scattering of relevant information across many databases. The three knowledge sources are the Metathesaurus, the Semantic Network, and the Specialist Lexicon.
| Descriptor ID |
D017432
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| MeSH Number(s) |
L01.453.245.945.800
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| Concept/Terms |
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Below are MeSH descriptors whose meaning is more general than "Unified Medical Language System".
Below are MeSH descriptors whose meaning is more specific than "Unified Medical Language System".
This graph shows the total number of publications written about "Unified Medical Language System" by people in this website by year, and whether "Unified Medical Language System" was a major or minor topic of these publications.
To see the data from this visualization as text, click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 2003 | 0 | 1 | 1 | | 2006 | 0 | 1 | 1 | | 2011 | 2 | 0 | 2 | | 2020 | 1 | 0 | 1 |
To return to the timeline, click here.
Below are the most recent publications written about "Unified Medical Language System" by people in Profiles.
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Kulshrestha S, Dligach D, Joyce C, Baker MS, Gonzalez R, O'Rourke AP, Glazer JM, Stey A, Kruser JM, Churpek MM, Afshar M. Prediction of severe chest injury using natural language processing from the electronic health record. Injury. 2021 Feb; 52(2):205-212.
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Xu D, Gopale M, Zhang J, Brown K, Begoli E, Bethard S. Unified Medical Language System resources improve sieve-based generation and Bidirectional Encoder Representations from Transformers (BERT)-based ranking for concept normalization. J Am Med Inform Assoc. 2020 10 01; 27(10):1510-1519.
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Sharma B, Dligach D, Swope K, Salisbury-Afshar E, Karnik NS, Joyce C, Afshar M. Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients. BMC Med Inform Decis Mak. 2020 04 29; 20(1):79.
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Afshar M, Dligach D, Sharma B, Cai X, Boyda J, Birch S, Valdez D, Zelisko S, Joyce C, Modave F, Price R. Development and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies. J Am Med Inform Assoc. 2019 11 01; 26(11):1364-1369.
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Afshar M, Joyce C, Oakey A, Formanek P, Yang P, Churpek MM, Cooper RS, Zelisko S, Price R, Dligach D. A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning. AMIA Annu Symp Proc. 2018; 2018:157-165.
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Kim TY, Coenen A, Hardiker N, Bartz CC. Representation of nursing terminologies in UMLS. AMIA Annu Symp Proc. 2011; 2011:709-14.
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Kim TY, Coenen A, Hardiker N. Semantic mappings and locality of nursing diagnostic concepts in UMLS. J Biomed Inform. 2012 Feb; 45(1):93-100.
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Bada M, Hunter L. Enrichment of OBO ontologies. J Biomed Inform. 2007 Jun; 40(3):300-15.
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Hardiker NR. Logical ontology for mediating between nursing intervention terminology systems. Methods Inf Med. 2003; 42(3):265-70.
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