title: Deriving comorbidities from medical records using natural language processing.
abstract: Extracting comorbidity information is crucial for phenotypic studies because of the confounding effect of comorbidities. We developed an automated method that accurately determines comorbidities from electronic medical records. Using a modified version of the Charlson comorbidity index (CCI), two physicians created a reference standard of comorbidities by manual review of 100 admission notes. We processed the notes using the MedLEE natural language processing system, and wrote queries to extract comorbidities automatically from its structured output. Interrater agreement for the reference set was very high (97.7%). Our method yielded an F1 score of 0.761 and the summed CCI score was not different from the reference standard (p=0.329, power 80.4%). In comparison, obtaining comorbidities from claims data yielded an F1 score of 0.741, due to lower sensitivity (66.1%). Because CCI has previously been validated as a predictor of mortality and readmission, our method could allow automated prediction of these outcomes.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, USA.
authors.forename: Hojjat
authors.initials: H
authors.lastname: Salmasian
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Freedberg
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D015897:Comorbidity; D048909:Diabetes Complications; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D009323:Natural Language Processing; D012015:Reference Standards; D012680:Sensitivity and Specificity; D055815:Young Adult
keywords: Comorbidity; Confounding Factors; Natural Language Processing