Literature DB >> 9357672

Dynamic organization of search results using the UMLS.

W Pratt1.   

Abstract

When people search the medical literature, they often are overwhelmed by the large number of documents retrieved. Many systems try to solve this problem by helping the user formulate a more specific search strategy. However, when users do not have a more specific question, they need tools to help them explore and understand the results, rather than to eliminate a portion of those results. This paper describes an approach that addresses this need by automatically grouping the results of a broad search into meaningful categories based on the user's query. This approach combines the main benefit of clustering techniques with the main benefit of classification techniques by taking advantage of the domain knowledge present in the UMLS. I present a preliminary evaluation that demonstrates that a categorization produced by this approach corresponds reasonably well to a physician's categorization.

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Mesh:

Year:  1997        PMID: 9357672      PMCID: PMC2233468     

Source DB:  PubMed          Journal:  Proc AMIA Annu Fall Symp        ISSN: 1091-8280


  4 in total

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Journal:  Bull Med Libr Assoc       Date:  1993-04

2.  UMLS knowledge for biomedical language processing.

Authors:  A T McCray; A R Aronson; A C Browne; T C Rindflesch; A Razi; S Srinivasan
Journal:  Bull Med Libr Assoc       Date:  1993-04

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Authors:  J Fowler; V Kouramajian; S Maram; V Devadhar
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1995

4.  Indexing consistency in MEDLINE.

Authors:  M E Funk; C A Reid
Journal:  Bull Med Libr Assoc       Date:  1983-04
  4 in total
  8 in total

1.  QueryCat: automatic categorization of MEDLINE queries.

Authors:  W Pratt; H Wasserman
Journal:  Proc AMIA Symp       Date:  2000

2.  Automated indexing for full text information retrieval.

Authors:  D C Berrios
Journal:  Proc AMIA Symp       Date:  2000

3.  The usefulness of dynamically categorizing search results.

Authors:  W Pratt; L Fagan
Journal:  J Am Med Inform Assoc       Date:  2000 Nov-Dec       Impact factor: 4.497

4.  Aggregating UMLS semantic types for reducing conceptual complexity.

Authors:  A T McCray; A Burgun; O Bodenreider
Journal:  Stud Health Technol Inform       Date:  2001

5.  Methods for semi-automated indexing for high precision information retrieval.

Authors:  Daniel C Berrios; Russell J Cucina; Lawrence M Fagan
Journal:  J Am Med Inform Assoc       Date:  2002 Nov-Dec       Impact factor: 4.497

6.  AskHERMES: An online question answering system for complex clinical questions.

Authors:  YongGang Cao; Feifan Liu; Pippa Simpson; Lamont Antieau; Andrew Bennett; James J Cimino; John Ely; Hong Yu
Journal:  J Biomed Inform       Date:  2011-01-21       Impact factor: 6.317

7.  SKIMMR: facilitating knowledge discovery in life sciences by machine-aided skim reading.

Authors:  Vít Nováček; Gully A P C Burns
Journal:  PeerJ       Date:  2014-07-22       Impact factor: 2.984

8.  Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search.

Authors:  Yanqing Ji; Hao Ying; John Tran; Peter Dews; R Michael Massanari
Journal:  BMC Bioinformatics       Date:  2016-07-19       Impact factor: 3.169

  8 in total

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