title: Identifying wrist fracture patients with high accuracy by automatic categorization of X-ray reports.
abstract: 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.
authors.affiliation: NRC-IIT, 1200 Montreal Road, Building M-50, Ottawa ON, Canada. berry.debruijn@nrc.gc.ca
authors.forename: Berry
authors.initials: B
authors.lastname: de Bruijn
authors.forename: Ann
authors.initials: A
authors.lastname: Cranney
authors.forename: Siobhan
authors.initials: S
authors.lastname: O'Donnell
authors.forename: Joel D
authors.initials: JD
authors.lastname: Martin
authors.forename: Alan J
authors.initials: AJ
authors.lastname: Forster
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D006801:Humans; D016247:Information Storage and Retrieval / Q000145:classification; D008499:Medical Records / Q000145:classification; D016571:Neural Networks, Computer; D011859:Radiography; D011873:Radiology Information Systems; D014954:Wrist Injuries / Q000000981:diagnostic imaging*