The Yale cTAKES extensions for document classification: Architecture and application

Background: Open-source clinical natural-languageprocessing (NLP) systems have lowered the barrier to the development of effective clinical document classification systems. Clinical natural-languageprocessing 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.