title: Automatic identification of critical follow-up recommendation sentences in radiology reports.
abstract: Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. When recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. Using information technology can improve communication and improve patient safety. In this paper, we describe a text processing approach that uses natural language processing (NLP) and supervised text classification methods to automatically identify critical recommendation sentences in radiology reports. To increase the classification performance we enhanced the simple unigram token representation approach with lexical, semantic, knowledge-base, and structural features. We tested different combinations of those features with the Maximum Entropy (MaxEnt) classification algorithm. Classifiers were trained and tested with a gold standard corpus annotated by a domain expert. We applied 5-fold cross validation and our best performing classifier achieved 95.60% precision, 79.82% recall, 87.0% F-score, and 99.59% classification accuracy in identifying the critical recommendation sentences in radiology reports.
authors.affiliation: Biomedical & Health Informatics, School of Medicine, University of Washington, Seattle, WA, USA.
authors.forename: Meliha
authors.initials: M
authors.lastname: Yetisgen-Yildiz
authors.forename: Martin L
authors.initials: ML
authors.lastname: Gunn
authors.forename: Fei
authors.initials: F
authors.lastname: Xia
authors.forename: Thomas H
authors.initials: TH
authors.lastname: Payne
mesh_terms: D000465:Algorithms; D057286:Electronic Health Records / Q000145:classification; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D051188:Knowledge Bases; D009323:Natural Language Processing; D011871:Radiology / Q000379:methods; D011873:Radiology Information Systems / Q000145:classification; D012660:Semantics; D017432:Unified Medical Language System