Comparing methods for identifying pancreatic cancer patients using electronic data sources

Jeff Friedlin, Marc Overhage, Mohammed A. Al-Haddad, Joshua A. Waters, J. Juan R Aguilar-Saavedra, Joe Kesterson, C. Schmidt

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

We sought to determine the accuracy of two electronic methods of identifying pancreatic cancer in a cohort of pancreatic cyst patients, and to examine the reasons for identification failure. We used the International Classification of Diseases, 9(th) Edition (ICD-9) codes and natural language processing (NLP) technology to identify pancreatic cancer in these patients. We compared both methods to a human-validated gold-standard surgical database. Both ICD-9 codes and NLP technology achieved high sensitivity for identifying pancreatic cancer, but the ICD-9 code method achieved markedly lower specificity and PPV compared to the NLP method. The NLP method required only slightly greater expenditures of time and effort compared to the ICD-9 code method. We identified several variables influencing the accuracy of ICD-9 codes to identify cancer patients including: the identification algorithm, kind of cancer to be identified, presence of other conditions similar to cancer, and presence of conditions that are precancerous.

Original languageEnglish (US)
Pages (from-to)237-241
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2010
StatePublished - 2010

ASJC Scopus subject areas

  • Medicine(all)

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    Friedlin, J., Overhage, M., Al-Haddad, M. A., Waters, J. A., Aguilar-Saavedra, J. J. R., Kesterson, J., & Schmidt, C. (2010). Comparing methods for identifying pancreatic cancer patients using electronic data sources. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2010, 237-241.