Background#R##N#Radiology reports are a rich resource for biomedical research. Prior to utilization, trained experts must manually review reports to identify discrete outcomes. The Audiological and Genetic Database (AudGenDB) is a public, de-identified research database that contains over 16,000 radiology reports. Because the reports are unlabeled, it is difficult to select those with specific abnormalities. We implemented a classification pipeline using a human-in-the-loop machine learning approach and open source libraries to label the reports with one or more of four abnormality region labels: inner, middle, outer, and mastoid, indicating the presence of an abnormality in the specified ear region.
['Aaron J. Masino', 'Robert W. Grundmeier', 'Jeffrey W. Pennington', 'John A. Germiller', 'E. Bryan Crenshaw']
Temporal bone radiology report classification using open source machine learning and natural langue processing libraries