title: Extracting actionable findings of appendicitis from radiology reports using natural language processing.
abstract: Radiology reports often contain findings about the condition of a patient which should be acted upon quickly. These actionable findings in a radiology report can be automatically detected to ensure that the referring physician is notified about such findings and to provide feedback to the radiologist that further action has been taken. In this paper we investigate a method for detecting actionable findings of appendicitis in radiology reports. The method identifies both individual assertions regarding the presence of appendicitis and other findings related to appendicitis using syntactic dependency patterns. All relevant individual statements from a report are collectively considered to determine whether the report is consistent with appendicitis. Evaluation on a corpus of 400 radiology reports annotated by two expert radiologists showed that our approach achieves a precision of 91%, a recall of 83%, and an F1-measure of 87%.
authors.affiliation: The University of Texas at Dallas, Richardson, TX, USA.
authors.forename: Bryan
authors.initials: B
authors.lastname: Rink
authors.forename: Kirk
authors.initials: K
authors.lastname: Roberts
authors.forename: Sanda
authors.initials: S
authors.lastname: Harabagiu
authors.forename: Richard H
authors.initials: RH
authors.lastname: Scheuermann
authors.forename: Seth
authors.initials: S
authors.lastname: Toomay
authors.forename: Travis
authors.initials: T
authors.lastname: Browning
authors.forename: Teresa
authors.initials: T
authors.lastname: Bosler
authors.forename: Ronald
authors.initials: R
authors.lastname: Peshock