title: Improving case definition of Crohn's disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach.
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.
authors.affiliation: Gastrointestinal Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA. aananthakrishnan@partners.org
authors.forename: Ashwin N
authors.initials: AN
authors.lastname: Ananthakrishnan
authors.forename: Tianxi
authors.initials: T
authors.lastname: Cai
authors.forename: Guergana
authors.initials: G
authors.lastname: Savova
authors.forename: Su-Chun
authors.initials: SC
authors.lastname: Cheng
authors.forename: Pei
authors.initials: P
authors.lastname: Chen
authors.forename: Raul Guzman
authors.initials: RG
authors.lastname: Perez
authors.forename: Vivian S
authors.initials: VS
authors.lastname: Gainer
authors.forename: Shawn N
authors.initials: SN
authors.lastname: Murphy
authors.forename: Peter
authors.initials: P
authors.lastname: Szolovits
authors.forename: Zongqi
authors.initials: Z
authors.lastname: Xia
authors.forename: Stanley
authors.initials: S
authors.lastname: Shaw
authors.forename: Susanne
authors.initials: S
authors.lastname: Churchill
authors.forename: Elizabeth W
authors.initials: EW
authors.lastname: Karlson
authors.forename: Isaac
authors.initials: I
authors.lastname: Kohane
authors.forename: Robert M
authors.initials: RM
authors.lastname: Plenge
authors.forename: Katherine P
authors.initials: KP
authors.lastname: Liao
mesh_terms: D000328:Adult; D000465:Algorithms; D003093:Colitis, Ulcerative / Q000175:diagnosis*; D003424:Crohn Disease / Q000175:diagnosis*; D057286:Electronic Health Records; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008490:Medical Informatics; D009323:Natural Language Processing; D011379:Prognosis; D062606:Tertiary Care Centers