title: Automated Classification of Radiology Reports for Acute Lung Injury: Comparison of Keyword and Machine Learning Based Natural Language Processing Approaches.
abstract: This paper compares the performance of keyword and machine learning-based chest x-ray report classification for Acute Lung Injury (ALI). ALI mortality is approximately 30 percent. High mortality is, in part, a consequence of delayed manual chest x-ray classification. An automated system could reduce the time to recognize ALI and lead to reductions in mortality. For our study, 96 and 857 chest x-ray reports in two corpora were labeled by domain experts for ALI. We developed a keyword and a Maximum Entropy-based classification system. Word unigram and character n-grams provided the features for the machine learning system. The Maximum Entropy algorithm with character 6-gram achieved the highest performance (Recall=0.91, Precision=0.90 and F-measure=0.91) on the 857-report corpus. This study has shown that for the classification of ALI chest x-ray reports, the machine learning approach is superior to the keyword based system and achieves comparable results to highest performing physician annotators.
authors.affiliation: Department of Medical Education and Biomedical Informatics, University of Washington, Seattle WA.
authors.forename: Imre
authors.initials: I
authors.lastname: Solti
authors.forename: Colin R
authors.initials: CR
authors.lastname: Cooke
authors.forename: Fei
authors.initials: F
authors.lastname: Xia
authors.forename: Mark M
authors.initials: MM
authors.lastname: Wurfel