title: Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES).
abstract: We coded 700 radiology reports from 373 women using an unmodified deployment of the Cancer Text Information Extraction System (caTIES), a publicly-available tool using natural language processing techniques. We were moderately successfully using caTIES for case ascertainment, successfully identifying 9/11 of a random sample of cancer case (sensitivity 82%) and 5/100 controls (specificity 95%) We are currently developing a classification scheme to assess clinical risk of ovarian cancer and identifying required extensions to caTIES algorithms.
authors.affiliation: Group Health Cooperative, Center for Health Studies, Seattle, WA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Carrell
authors.forename: Diana
authors.initials: D
authors.lastname: Miglioretti
authors.forename: Rebecca
authors.initials: R
authors.lastname: Smith-Bindman
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods; D000465:Algorithms; D005260:Female; D005568:Forms and Records Control / Q000379:methods*; D006801:Humans; D009323:Natural Language Processing; D009369:Neoplasms / Q000145:classification*; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging; D011859:Radiography; D011873:Radiology Information Systems; D018875:Vocabulary, Controlled