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The Yale cTAKES extensions for document classification: architecture and application.

Submitted by holger on Thu, 2013/04/11 - 17:41
TitleThe Yale cTAKES extensions for document classification: architecture and application.
Publication TypeJournal Article
Year of Publication2011
AuthorsGarla, V, Re, VLo, Dorey-Stein, Z, Kidwai, F, Scotch, M, Womack, J, Justice, A, Brandt, C
JournalJ Am Med Inform Assoc
Date Published2011 Sep-Oct
KeywordsConnecticut, Data Mining, Decision Support Systems, Clinical, electronic health records, Humans, Liver Failure, Natural Language Processing, Pattern Recognition, Automated, Radiology Information Systems

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

Alternate JournalJ Am Med Inform Assoc
PubMed ID21622934
PubMed Central IDPMC3168305
Grant ListK01 AI 070001 / AI / NIAID NIH HHS / United States
K01 AI070001 / AI / NIAID NIH HHS / United States
U10 AA 13566 / AA / NIAAA NIH HHS / United States
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