Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES).
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.
Carrell
David
D
Group Health Cooperative, Center for Health Studies, Seattle, WA, USA.
Miglioretti
Diana
D
Smith-Bindman
Rebecca
R
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