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 |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES). / Carrell, David; Miglioretti, Diana L; Smith-Bindman, Rebecca.
In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2007, p. 889.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES).
AU - Carrell, David
AU - Miglioretti, Diana L
AU - Smith-Bindman, Rebecca
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=56149111869&partnerID=8YFLogxK
M3 - Article
C2 - 18693990
AN - SCOPUS:56149111869
SP - 889
JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
SN - 1559-4076
ER -