Machine-learned solutions for three stages of clinical information extraction : the state of the art at i2b2 2010

Download
  1. (PDF, 260 KB)
  2. Get@NRC: Machine-learned solutions for three stages of clinical information extraction : the state of the art at i2b2 2010 (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1136/amiajnl-2011-000150
AuthorSearch for: ; Search for: ; Search for: ; Search for: ; Search for:
TypeArticle
Journal titleJournal of the American Medical Informatics Association
Volume18
Issue5
Pages557562; # of pages: 6
AbstractAs clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality. A critical part of that process is rigid benchmark testing of natural language processing methods on realistic clinical narrative. In this paper, the authors describe the design and performance of three state-of-the-art text-mining applications from the National Research Council of Canada on evaluations within the 2010 i2b2 challenge.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NPARC number19688665
Export citationExport as RIS
Report a correctionReport a correction
Record identifierf659c8ba-d746-4b39-9d1b-eb7edd9641b3
Record created2012-03-21
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)
Date modified: