Improving Case Definition of Crohns Disease and Ulcerative Colitis in Electronic Medical Records Using Natural Language Processing

Introduction—Prior studies identifying patients with inflammatory bowel disease (IBD) utilizing administrative codes have yielded inconsistent results. Our objective was to develop a robust electronic medical record (EMR) based model for classification of IBD leveraging the combination of codified data and information from clinical text notes using natural language processing (NLP). Methods—Using the EMR of 2 large academic centers, we created data marts for Crohn’s disease (CD) and ulcerative colitis (UC) comprising patients with ≥ 1 ICD-9 code for each disease. We utilized codified (i.e. ICD9 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 (67%) CD cases in the CD training set and 378 (63%) UC cases in the UC training set. For both, a combined model including narrative and codified data had better accuracy (area under the curve (AUC) for CD 0.95; UC 0.94) than models utilizing only disease Corresponding Author: Ashwin N Ananthakrishnan, MD, MPH, Crohn’s and Colitis center, Massachusetts General Hospital, 165 Cambridge Street, 9th Floor, Boston, MA 02114, Phone: 617-726-0267, Fax: 617-726-3080, [email protected]. The work was presented in part at the Digestive Disease Week 2012, May 19-22, San Diego, CA. NIH Public Access Author Manuscript Inflamm Bowel Dis. Author manuscript; available in PMC 2014 June 01. Published in final edited form as: Inflamm Bowel Dis. 2013 June ; 19(7): 1411–1420. doi:10.1097/MIB.0b013e31828133fd. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript ICD-9 codes (AUC 0.89 for CD; 0.86 for UC). Addition of NLP narrative terms to our final model resulted in classification of 6–12% more subjects with the same accuracy. Conclusion—Inclusion of narrative concepts identified using NLP improves the accuracy of EMR case-definition for CD and UC while simultaneously identifying more subjects compared to models using codified data alone.