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. 2010 Nov 13:2010:237-41.

Comparing methods for identifying pancreatic cancer patients using electronic data sources

Jeff Friedlin  1 , Marc Overhage, Mohammed A Al-Haddad, Joshua A Waters, J Juan R Aguilar-Saavedra, Joe Kesterson, Max Schmidt
Affiliations

Comparing methods for identifying pancreatic cancer patients using electronic data sources

Jeff Friedlin et al. AMIA Annu Symp Proc. .

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.

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Figures

Figure 1.
Figure 1.
Examples of Concept-Context couplets produced by REX
Figure 2.
Figure 2.
Accuracy of identifying pancreatic cancer patients by ICD-9 code and NLP compared to gold standard. (N=211)
Figure 3
Figure 3
Analysis of the false positive errors committed by the ICD-9 code and NLP methods.
See this image and copyright information in PMC

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