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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 889 |
| Number of pages | 1 |
| Journal | AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium |
| State | Published - 2007 |
| Externally published | Yes |
ASJC Scopus subject areas
- Medicine(all)
Fingerprint Dive into the research topics of 'Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES).'. Together they form a unique fingerprint.
Cite this
Carrell, D., Miglioretti, D. L., & Smith-Bindman, R. (2007). Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES). AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 889.