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Identifying Wrist Fracture Patients with High Accuracy by Automatic Categorization of X-ray Reports

^a National Research Council Canada, Institute for Information Technology
^b Ottawa Hospital Research Institute
^c Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada

^_* Correspondence and reprints: Berry de Bruijn, Ph.D., NRC-IIT, 1200 Montreal Road, Building M-50, Ottawa ON, Canada K1A 0R6. (Email: berry.debruijn{at}nrc.gc.ca).

Received for publication: 10/21/05; accepted for publication: 07/10/06.

The authors performed this study to determine the accuracy of several text classification methods to categorize wrist x-ray reports. We randomly sampled 751 textual wrist x-ray reports. Two expert reviewers rated the presence (n = 301) or absence (n = 450) of an acute fracture of wrist. We developed two information retrieval (IR) text classification methods and a machine learning method using a support vector machine (TC-1). In cross-validation on the derivation set (n = 493), TC-1 outperformed the two IR based methods and six benchmark classifiers, including Naive Bayes and a Neural Network. In the validation set (n = 258), TC-1 demonstrated consistent performance with 93.8% accuracy; 95.5% sensitivity; 92.9% specificity; and 87.5% positive predictive value. TC-1 was easy to implement and superior in performance to the other classification methods.

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