title: The Yale cTAKES extensions for document classification: architecture and application.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, Connecticut, USA. vijay.garla@yale.edu
authors.forename: Vijay
authors.initials: V
authors.lastname: Garla
authors.forename: Vincent
authors.initials: V
authors.lastname: Lo Re
authors.forename: Zachariah
authors.initials: Z
authors.lastname: Dorey-Stein
authors.forename: Farah
authors.initials: F
authors.lastname: Kidwai
authors.forename: Matthew
authors.initials: M
authors.lastname: Scotch
authors.forename: Julie
authors.initials: J
authors.lastname: Womack
authors.forename: Amy
authors.initials: A
authors.lastname: Justice
authors.forename: Cynthia
authors.initials: C
authors.lastname: Brandt
mesh_terms: D003237:Connecticut; D057225:Data Mining / Q000145:classification; D020000:Decision Support Systems, Clinical / Q000145:classification; D057286:Electronic Health Records / Q000145:classification; D006801:Humans; D017093:Liver Failure / Q000000981:diagnostic imaging; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000145:classification; D011859:Radiography; D011873:Radiology Information Systems / Q000145:classification