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

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1016/j.jbi.2012.11.006
AuteurRechercher : 1; Rechercher : 1; Rechercher : 1; Rechercher : 1; Rechercher : 1
Affiliation
  1. Conseil national de recherches du Canada. Institut de technologie de l'information du CNRC
FormatTexte, Article
SujetElectronic 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; data extraction; kernel method; semantics
Résumé
This 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.
Date de publication
Dans
  • Journal of Biomedical Informatics 46, nº 2 (2013) : 275–285.
Langueanglais
Publications évaluées par des pairsOui
Numéro NPARC21269982
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Identificateur de l’enregistrement6e865eaf-0f5a-43e7-82f4-ae2d16f652c5
Enregistrement créé2013-12-13
Enregistrement modifié2020-04-22
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