title: Automated mutual exclusion rules discovery for structured observational codes in echocardiography reporting.
abstract: Structured reporting in medicine has been argued to support and enhance machine-assisted processing and communication of pertinent information. Retrospective studies showed that structured echocardiography reports, constructed through point-and-click selection of finding codes (FCs), contain pair-wise contradictory FCs (e.g., "No tricuspid regurgitation" and "Severe regurgitation") downgrading report quality and reliability thereof. In a prospective study, contradictions were detected automatically using an extensive rule set that encodes mutual exclusion patterns between FCs. Rules creation is a labor and knowledge-intensive task that could benefit from automation. We propose a machine-learning approach to discover mutual exclusion rules in a corpus of 101,211 structured echocardiography reports through semantic and statistical analysis. Ground truth is derived from the extensive prospectively evaluated rule set. On the unseen test set, F-measure (0.439) and above-chance level AUC (0.885) show that our approach can potentially support the manual rules creation process. Our methods discovered previously unknown rules per expert review.
authors.affiliation: Philips Research Europe, Suresnes, France.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Forsberg
authors.affiliation: Philips Research North America, Briarcliff Manor, NY.
authors.forename: Merlijn
authors.initials: M
authors.lastname: Sevenster
authors.affiliation: Philips Healthcare, Andover, MA.
authors.forename: Szymon
authors.initials: S
authors.lastname: Bieganski
authors.affiliation: Philips Research North America, Briarcliff Manor, NY.
authors.forename: Puran
authors.initials: P
authors.lastname: Bhagat
authors.affiliation: Philips Research North America, Briarcliff Manor, NY.
authors.forename: Melvin
authors.initials: M
authors.lastname: Kanasseril
authors.affiliation: Philips Research North America, Briarcliff Manor, NY.
authors.forename: Yugang
authors.initials: Y
authors.lastname: Jia
authors.affiliation: University of Chicago, Chicago, IL.
authors.forename: Kirk T
authors.initials: KT
authors.lastname: Spencer
mesh_terms: D019540:Area Under Curve; D057225:Data Mining / Q000379:methods*; D003951:Diagnostic Errors; D004452:Echocardiography; D006801:Humans; D000069550:Machine Learning; D011446:Prospective Studies; D015203:Reproducibility of Results