Detecting concept relations in clinical text: Insights from a state-of-the-art model

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DOIResolve DOI: http://doi.org/10.1016/j.jbi.2012.11.006
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TypeArticle
Journal titleJournal of Biomedical Informatics
ISSN1532-0464
Volume46
Issue2
Pages275285; # of pages: 11
SubjectElectronic health record; Knowledge sources; Medical concepts; NAtural language processing; Real-world scenario; Semantic relations; State of the art; Text mining; Algorithms; Artificial intelligence; Data mining; Medical problems; Semantics; Natural language processing systems; accuracy; article; data extraction; human; kernel method; priority journal; semantics
AbstractThis paper addresses an information-extraction problem that aims to identify semantic relations among medical concepts (problems, tests, and treatments) in clinical text. The objectives of the paper are twofold. First, we extend an earlier one-page description (appearing as a part of [5]) of a top-ranked model in the 2010 I2B2 NLP Challenge to a necessary level of details, with the belief that feature design is the most crucial factor to the success of our system and hence deserves a more detailed discussion. We present a precise quantification of the contributions of a wide variety of knowledge sources. In addition, we show the end-to-end results obtained on the noisy output of a top-ranked concept detector, which could help construct a more complete view of the state of the art in the real-world scenario. As the second major objective, we reformulate our models into a composite-kernel framework and present the best result, according to our knowledge, on the same dataset. © 2012.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology (IIT-ITI)
Peer reviewedYes
NPARC number21269982
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Record identifier6e865eaf-0f5a-43e7-82f4-ae2d16f652c5
Record created2013-12-13
Record modified2016-05-09
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