Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES).

David Carrell, Diana L Miglioretti, Rebecca Smith-Bindman

Research output: Contribution to journalArticle

15 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)889
Number of pages1
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2007
Externally publishedYes

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Information Storage and Retrieval
Radiology
Information Systems
Neoplasms
Natural Language Processing
Ovarian Neoplasms

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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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.",
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