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The Unified Medical Language System

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Publisher Thieme
Date 2016 Sep 27
PMID 27668467
Citations 33
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Abstract

In 1986, the National Library of Medicine began a long-term research and development project to build the Unified Medical Language System® (UMLS®). The purpose of the UMLS is to improve the ability of computer programs to "understand" the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users. Underlying the UMLS effort is the assumption that timely access to accurate and up-to-date information will improve decision making and ultimately the quality of patient care and research. The development of the UMLS is a distributed national experiment with a strong element of international collaboration. The general strategy is to develop UMLS components through a series of successive approximations of the capabilities ultimately desired. Three experimental Knowledge Sources, the Metathesaurus®, the Semantic Network, and the Information Sources Map have been developed and are distributed annually to interested researchers, many of whom have tested and evaluated them in a range of applications. The UMLS project and current developments in high-speed, high-capacity international networks are converging in ways that have great potential for enhancing access to biomedical information.

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References
1.
Campbell J, Kallenberg G, Sherrick R . The clinical utility of META: an analysis for hypertension. Proc Annu Symp Comput Appl Med Care. 1992; :397-401. PMC: 2248117. View

2.
Clyman J, Powsner S, Paton J, Miller P . Using a network menu and the UMLS Information Sources Map to facilitate access to online reference materials. Bull Med Libr Assoc. 1993; 81(2):207-16. PMC: 225763. View

3.
Friedman C . The UMLS coverage of clinical radiology. Proc Annu Symp Comput Appl Med Care. 1992; :309-13. PMC: 2248002. View

4.
Kingsland 3rd L, Harbourt A, Syed E, Schuyler P . Coach: applying UMLS knowledge sources in an expert searcher environment. Bull Med Libr Assoc. 1993; 81(2):178-83. PMC: 225760. View

5.
Bunting A . The Nation's Health Information Network: History of the Regional Medical Library Program, 1965-1985. Bull Med Libr Assoc. 1987; 75(3 Suppl):1-62. PMC: 280609. View