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Randomized Controlled Trial
. 2013 Jun;19(7):1411-20.
doi: 10.1097/MIB.0b013e31828133fd.

Improving case definition of Crohn's disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach

Ashwin N Ananthakrishnan  1 , Tianxi Cai, Guergana Savova, Su-Chun Cheng, Pei Chen, Raul Guzman Perez, Vivian S Gainer, Shawn N Murphy, Peter Szolovits, Zongqi Xia, Stanley Shaw, Susanne Churchill, Elizabeth W Karlson, Isaac Kohane, Robert M Plenge, Katherine P Liao
Affiliations
Randomized Controlled Trial

Improving case definition of Crohn's disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach

Ashwin N Ananthakrishnan et al. Inflamm Bowel Dis. 2013 Jun.

Abstract

Background: Previous studies identifying patients with inflammatory bowel disease using administrative codes have yielded inconsistent results. Our objective was to develop a robust electronic medical record-based model for classification of inflammatory bowel disease leveraging the combination of codified data and information from clinical text notes using natural language processing.

Methods: Using the electronic medical records of 2 large academic centers, we created data marts for Crohn's disease (CD) and ulcerative colitis (UC) comprising patients with ≥1 International Classification of Diseases, 9th edition, code for each disease. We used codified (i.e., International Classification of Diseases, 9th edition codes, electronic prescriptions) and narrative data from clinical notes to develop our classification model. Model development and validation was performed in a training set of 600 randomly selected patients for each disease with medical record review as the gold standard. Logistic regression with the adaptive LASSO penalty was used to select informative variables.

Results: We confirmed 399 CD cases (67%) in the CD training set and 378 UC cases (63%) in the UC training set. For both, a combined model including narrative and codified data had better accuracy (area under the curve for CD 0.95; UC 0.94) than models using only disease International Classification of Diseases, 9th edition codes (area under the curve 0.89 for CD; 0.86 for UC). Addition of natural language processing narrative terms to our final model resulted in classification of 6% to 12% more subjects with the same accuracy.

Conclusions: Inclusion of narrative concepts identified using natural language processing improves the accuracy of electronic medical records case definition for CD and UC while simultaneously identifying more subjects compared with models using codified data alone.

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Figures

Figure 1
Figure 1
Classification model for defining inflammatory bowel disease cohorts in the electronic medical record cohort
Figure 2
Figure 2
Figure 2a: Comparison of codified data and narrative mentions of disease complications, medications and outcomes in confirmed Crohn’s disease patients in the training set (n = 399) Figure 2b: Comparison of codified data and narrative mentions of medications and outcomes in confirmed ulcerative colitis patients in the training set (n = 378)
Figure 2
Figure 2
Figure 2a: Comparison of codified data and narrative mentions of disease complications, medications and outcomes in confirmed Crohn’s disease patients in the training set (n = 399) Figure 2b: Comparison of codified data and narrative mentions of medications and outcomes in confirmed ulcerative colitis patients in the training set (n = 378)
Figure 3
Figure 3
Figure 3a: Beta-coefficients of significant predictors included in the final combined model for Crohn’s disease Figure 3b: Beta-coefficients of significant predictors including in the final model for ulcerative colitis
Figure 3
Figure 3
Figure 3a: Beta-coefficients of significant predictors included in the final combined model for Crohn’s disease Figure 3b: Beta-coefficients of significant predictors including in the final model for ulcerative colitis
Figure 4
Figure 4. Proportion of patients in the entire EMR data mart classified as having Crohn’s disease (CD) or ulcerative colitis (UC) with 97% specificity
The numbers over the bar graph represent the estimated size of our EMR cohort for CD and UC using each of the four models
See this image and copyright information in PMC

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