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Knowledge-based Approaches to the Maintenance of a Large Controlled Medical Terminology

Knowledge-based Approaches to the Maintenance of a Large Controlled Medical Terminology Abstract Objective: Develop a knowledge-based representation for a controlled terminology of clinical information to facilitate creation, maintenance, and use of the terminology. Design: The Medical Entities Dictionary (MED) is a semantic network, based on the Unified Medical Language System (UMLS), with a directed acyclic graph to represent multiple hierarchies. Terms from four hospital systems (laboratory, electrocardiography, medical records coding, and pharmacy) were added as nodes in the network. Additional knowledge about terms, added as semantic links, was used to assist in integration, harmonization, and automated classification of disparate terminologies. Results: The MED contains 32,767 terms and is in active clinical use. Automated classification was successfully applied to terms for laboratory specimens, laboratory tests, and medications. One benefit of the approach has been the automated inclusion of medications into multiple pharmacologic and allergenic classes that were not present in the pharmacy system. Another benefit has been the reduction of maintenance efforts by 90%. Conclusion: The MED is a hybrid of terminology and knowledge. It provides domain coverage, synonymy, consistency of views, explicit relationships, and multiple classification while preventing redundancy, ambiguity (homonymy) and misclassification. This content is only available as a PDF. Author notes Supported under the National Library of Medicine's Interated Advanced Information Management Systems (IAIMS) project and by the IBM Corporation. Drs. Cimino and Johnson are supported in part through a Unified Medical Language System contract with the National Library of Medicine. American Medical Informatics Association http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

Knowledge-based Approaches to the Maintenance of a Large Controlled Medical Terminology

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Publisher
Oxford University Press
Copyright
American Medical Informatics Association
ISSN
1067-5027
eISSN
1527-974X
DOI
10.1136/jamia.1994.95236135
Publisher site
See Article on Publisher Site

Abstract

Abstract Objective: Develop a knowledge-based representation for a controlled terminology of clinical information to facilitate creation, maintenance, and use of the terminology. Design: The Medical Entities Dictionary (MED) is a semantic network, based on the Unified Medical Language System (UMLS), with a directed acyclic graph to represent multiple hierarchies. Terms from four hospital systems (laboratory, electrocardiography, medical records coding, and pharmacy) were added as nodes in the network. Additional knowledge about terms, added as semantic links, was used to assist in integration, harmonization, and automated classification of disparate terminologies. Results: The MED contains 32,767 terms and is in active clinical use. Automated classification was successfully applied to terms for laboratory specimens, laboratory tests, and medications. One benefit of the approach has been the automated inclusion of medications into multiple pharmacologic and allergenic classes that were not present in the pharmacy system. Another benefit has been the reduction of maintenance efforts by 90%. Conclusion: The MED is a hybrid of terminology and knowledge. It provides domain coverage, synonymy, consistency of views, explicit relationships, and multiple classification while preventing redundancy, ambiguity (homonymy) and misclassification. This content is only available as a PDF. Author notes Supported under the National Library of Medicine's Interated Advanced Information Management Systems (IAIMS) project and by the IBM Corporation. Drs. Cimino and Johnson are supported in part through a Unified Medical Language System contract with the National Library of Medicine. American Medical Informatics Association

Journal

Journal of the American Medical Informatics AssociationOxford University Press

Published: Jan 1, 1994

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