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- Title
The Yale cTAKES extensions for document classification: architecture and application.
- Authors
Garla, Vijay; Re, III, Vincent Lo; Dorey-Stein, Zachariah; Kidwai, Farah; Scotch, Matthew; Womack, Julie; Justice, Amy; Brandt, Cynthia
- 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. Results and discussion The F1-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.
- Subjects
CLASSIFICATION; NATURAL language processing; RULE-based programming; MACHINE learning; DATA mining; ALGORITHMS
- Publication
Journal of the American Medical Informatics Association, 2011, Vol 18, Issue 5, p614
- ISSN
1067-5027
- Publication type
Article
- DOI
10.1136/amiajnl-2011-000093