The Yale cTAKES extensions for document classification: architecture and application.
Background: Open-source clinical natural-language-processing (NLP) systems have lowered the barrier to the development of effective clinical document classification systems. Clinical natural-language-processing systems annotate the syntax and semantics of clinical text; however, feature extraction a...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 18; no. 5; pp. 614 - 621 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104577246&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104577246 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Sep2011 vid: 18 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104577246 NLM21622934 2011241444 10.1136/amiajnl-2011-000093 NLM21622934 PMC3168305 104577246 ppf: 614 ppct: 7 formats: tig: atl: The Yale cTAKES extensions for document classification: architecture and application. aug: au: Garla V Lo Re V 3rd Dorey-Stein Z Kidwai F Scotch M Womack J Justice A Brandt C Garla, Vijay Lo Re, Vincent 3rd Dorey-Stein, Zachariah Kidwai, Farah Scotch, Matthew Womack, Julie Justice, Amy Brandt, Cynthia affil: Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, Connecticut, USA sug: subj: Data Mining Classification Decision Support Systems, Clinical Classification Electronic Health Records Classification Natural Language Processing Information Science Classification Connecticut Liver Failure Radiography Radiology Information Systems Classification ab: Background: Open-source clinical natural-language-processing (NLP) systems have lowered the barrier to the development of effective clinical document classification systems. Clinical natural-language-processing systems annotate the syntax and semantics of clinical text; however, feature extraction and representation for document classification pose technical challenges.Methods: The authors developed extensions to the clinical Text Analysis and Knowledge Extraction System (cTAKES) that simplify feature extraction, experimentation with various feature representations, and the development of both rule and machine-learning based document classifiers. The authors describe and evaluate their system, the Yale cTAKES Extensions (YTEX), on the classification of radiology reports that contain findings suggestive of hepatic decompensation.Results and Discussion: The F(1)-Score of the system for the retrieval of abdominal radiology reports was 96%, and was 79%, 91%, and 95% for the presence of liver masses, ascites, and varices, respectively. The authors released YTEX as open source, available at http://code.google.com/p/ytex. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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