Identifying wrist fracture patients with high accuracy by automatic categorization of X-ray reports.
J Am Med Inform Assoc
; 13(6): 696-8, 2006.
Article
en En
| MEDLINE
| ID: mdl-16929046
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Traumatismos de la Muñeca
/
Inteligencia Artificial
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
J Am Med Inform Assoc
Asunto de la revista:
INFORMATICA MEDICA
Año:
2006
Tipo del documento:
Article
País de afiliación:
Canadá
Pais de publicación:
Reino Unido