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Facilitating biomedical systematic reviews using ranked text retrieval and classification

by: David Martinez, Sarvnaz Karimi, Lawrence Cavedon, and Timothy Baldwin

In: ADCS 2008 Proceedings Hobart: (2008) , p. 53--60.

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URL:	http://es.csiro.au/adcs2008/proceedings/p09-martinez.pdf

Searching and selecting articles to be included in systematic reviews is a real challenge for healthcare agencies responsible for publishing these reviews. The current practice of manually reviewing all papers returned by complex hand-crafted boolean queries is human labour-intensive and difficult to maintain. We demonstrate a two-stage searching system that takes advantage of ranked queries and support-vector machine text classification to assist in the retrieval of relevant articles, and to restrict results to higher-quality documents. Our proposed approach shows significant work saved in the systematic review process over a baseline of a keyword-based retrieval system.

BibTeX record

@inproceedings{Martinez:2008,
  abstract = {Searching and selecting articles to be included in systematic reviews is a real challenge for healthcare agencies responsible for publishing these reviews. The current practice of manually reviewing all papers returned by complex hand-crafted boolean queries is human labour-intensive and difficult to maintain. We demonstrate a two-stage searching system that takes advantage of ranked queries and support-vector machine text classification to assist in the retrieval of relevant articles, and to restrict results to higher-quality documents. Our proposed approach shows significant work saved in the systematic review process over a baseline of a keyword-based retrieval system.},
  added-at = {2010-01-12T08:06:20.000+0100},
  address = {Hobart},
  author = {Martinez, David and Karimi, Sarvnaz and Cavedon, Lawrence and Baldwin, Timothy},
  biburl = {http://www.bibsonomy.org/bibtex/2a233b1fcd321fc4e77e4d7e23159e64e/diego_ma},
  booktitle = {{ADCS} 2008 Proceedings},
  interhash = {9b316c89e34b84f2da105ebe37130670},
  intrahash = {a233b1fcd321fc4e77e4d7e23159e64e},
  keywords = {EBM inf_retrieval},
  pages = {53--60},
  timestamp = {2010-01-12T08:06:20.000+0100},
  title = {Facilitating biomedical systematic reviews using ranked text retrieval and classification},
  url = {http://es.csiro.au/adcs2008/proceedings/p09-martinez.pdf},
  year = 2008
}

Endnote record

%0 Conference Paper
%1 Martinez:2008
%A Martinez, David
%A Karimi, Sarvnaz
%A Cavedon, Lawrence
%A Baldwin, Timothy
%B {ADCS} 2008 Proceedings
%C Hobart
%D 2008
%K 
%P 53--60
%T Facilitating biomedical systematic reviews using ranked text retrieval and classification
%U http://es.csiro.au/adcs2008/proceedings/p09-martinez.pdf
%X Searching and selecting articles to be included in systematic reviews is a real challenge for healthcare agencies responsible for publishing these reviews. The current practice of manually reviewing all papers returned by complex hand-crafted boolean queries is human labour-intensive and difficult to maintain. We demonstrate a two-stage searching system that takes advantage of ranked queries and support-vector machine text classification to assist in the retrieval of relevant articles, and to restrict results to higher-quality documents. Our proposed approach shows significant work saved in the systematic review process over a baseline of a keyword-based retrieval system.

BibSonomy :: publication ::

Facilitating biomedical systematic reviews using ranked text retrieval and classification

Resources (URL, PDF, PS...)

Abstract

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Endnote record