title: Classification of CT pulmonary angiography reports by presence, chronicity, and location of pulmonary embolism with natural language processing.
abstract: In this paper we describe an efficient tool based on natural language processing for classifying the detail state of pulmonary embolism (PE) recorded in CT pulmonary angiography reports. The classification tasks include: PE present vs. absent, acute PE vs. others, central PE vs. others, and subsegmental PE vs. others. Statistical learning algorithms were trained with features extracted using the NLP tool and gold standard labels obtained via chart review from two radiologists. The areas under the receiver operating characteristic curves (AUC) for the four tasks were 0.998, 0.945, 0.987, and 0.986, respectively. We compared our classifiers with bag-of-words Naive Bayes classifiers, a standard text mining technology, which gave AUC 0.942, 0.765, 0.766, and 0.712, respectively.
authors.affiliation: Partners HealthCare Personalized Medicine, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, United States. Electronic address: syu7@partners.org.
authors.forename: Sheng
authors.initials: S
authors.lastname: Yu
authors.affiliation: Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, United States.
authors.forename: Kanako K
authors.initials: KK
authors.lastname: Kumamaru
authors.affiliation: Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, United States.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: George
authors.affiliation: Thoracic Imaging, Department of Radiology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, United States.
authors.forename: Ruth M
authors.initials: RM
authors.lastname: Dunne
authors.affiliation: Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, United States; Department of Radiology, University of Massachusetts Medical School, Worcester, MA, United States.
authors.forename: Arash
authors.initials: A
authors.lastname: Bedayat
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA, United States.
authors.forename: Matey
authors.initials: M
authors.lastname: Neykov
authors.affiliation: Thoracic Imaging, Department of Radiology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, United States.
authors.forename: Andetta R
authors.initials: AR
authors.lastname: Hunsaker
authors.affiliation: Department of Radiology, University of Chicago, Chicago, IL, United States.
authors.forename: Karin E
authors.initials: KE
authors.lastname: Dill
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA, United States.
authors.forename: Tianxi
authors.initials: T
authors.lastname: Cai
authors.affiliation: Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, United States.
authors.forename: Frank J
authors.initials: FJ
authors.lastname: Rybicki
mesh_terms: D000465:Algorithms; D000792:Angiography / Q000379:methods*; D006801:Humans; D009323:Natural Language Processing; D011655:Pulmonary Embolism / Q000000981:diagnostic imaging*; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: CT pulmonary angiography; NILE; Natural language processing; Nested modification structure; Pulmonary embolism