Document Detail


Empirical formulation of a generic query set for clinical information retrieval systems.
MedLine Citation:
PMID:  11604729     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Information needs in clinical practice take the form of specific questions about a given clinical situation, and are best satisfied by concise and specific information retrieval. We sought to develop a comprehensive set of generic queries for information retrieval from electronic medical information resources. We collected one hundred and ten real-world questions asked at the point of care in a variety of settings, and from these developed a set of generic queries of which each of the real-world queries could be shown to be a special case. To provide allowed values for each of the concept terms in the queries, we defined generic nouns as unions of UMLS semantic types, and specified which of these were appropriate to each query. We have begun to use the set to index reference texts from general and subspecialty medicine, and found it capable of full text indexing in the clinical domain. We hypothesize that the query set can serve as a basis for more specialized query sets, and that it will remain generalizable to other electronic medical resources, indexing tasks, and non-UMLS controlled vocabularies.
Authors:
R J Cucina; M K Shah; D C Berrios; L M Fagan
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Studies in health technology and informatics     Volume:  84     ISSN:  0926-9630     ISO Abbreviation:  Stud Health Technol Inform     Publication Date:  2001  
Date Detail:
Created Date:  2001-10-17     Completed Date:  2002-01-08     Revised Date:  2008-07-10    
Medline Journal Info:
Nlm Unique ID:  9214582     Medline TA:  Stud Health Technol Inform     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  181-5     Citation Subset:  IM    
Affiliation:
Stanford Medical Informatics, Department of Medicine, Stanford University, Stanford, California 94305, USA. 94305. rjcucina@stanford.edu
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MeSH Terms
Descriptor/Qualifier:
Abstracting and Indexing as Topic
Decision Support Systems, Clinical*
Humans
Information Storage and Retrieval / methods*
Patient Care
Unified Medical Language System

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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