Automatic identification of pneumonia related concepts on chest x-ray reports.
A medical language processing system called SymText, two other automated methods, and a lay person were compared against an internal medicine resident for their ability to identify pneumonia related concepts on chest x-ray reports. Sensitivity (recall), specificity, and positive predictive value (precision) are reported with respect to an independent panel of physicians. Overall the performance of SymText was similar to the physician and superior to the other methods. The automatic encoding of pneumonia concepts will support clinical research, decision making, computerized clinical protocols, and quality assurance in a radiology department.
Fiszman
M
M
Department of Medical Informatics, LDS Hospital, University of Utah, USA.
Chapman
W W
WW
Evans
S R
SR
Haug
P J
PJ
D001499:Bayes Theorem; D003658:Decision Making, Computer-Assisted; D005069:Evaluation Studies as Topic; D006751:Hospital Information Systems; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D007388:Internal Medicine; D009323:Natural Language Processing; D011014:Pneumonia / Q000000981:diagnostic imaging*; D013902:Radiography, Thoracic / Q000145:classification*; D012660:Semantics; D012680:Sensitivity and Specificity; D013358:Subject Headings