Electronic Medical Records for Discovery Research in Rheumatoid Arthritis
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Szolovits_Electronic medical.pdf
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Author(s)
Szolovits, Peter
Liao, Katherine P.
Cai, Tianxi
Gainer, Vivian
Goryachev, Sergey
Zeng-Treitler, Qing
Raychaudhuri, Soumya
Churchill, Susanne
Murphy, Shawn N.
Kohane, Isaac
Date Issued
March 2010
Journal
Arthritis Care & Research
Publisher
Wiley Blackwell (John Wiley & Sons)
Citation
Liao, Katherine P. et al. “Electronic Medical Records for Discovery Research in Rheumatoid Arthritis.” Arthritis Care & Research 62.8 (2010): 1120–1127.
Version
Author's final manuscript
Abstract
Objective:
Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with an algorithm using codified EMR data alone.
Methods:
Subjects with ≥1 International Classification of Diseases, Ninth Revision RA code (714.xx) or who had anti–cyclic citrullinated peptide (anti-CCP) checked in the EMR of 2 large academic centers were included in an “RA Mart” (n = 29,432). For all 29,432 subjects, we extracted narrative (using natural language processing) and codified RA clinical information. In a training set of 96 RA and 404 non-RA cases from the RA Mart classified by medical record review, we used narrative and codified data to develop classification algorithms using logistic regression. These algorithms were applied to the entire RA Mart. We calculated and compared the positive predictive value (PPV) of these algorithms by reviewing the records of an additional 400 subjects classified as having RA by the algorithms.
Results:
A complete algorithm (narrative and codified data) classified RA subjects with a significantly higher PPV of 94% than an algorithm with codified data alone (PPV of 88%). Characteristics of the RA cohort identified by the complete algorithm were comparable to existing RA cohorts (80% women, 63% anti-CCP positive, and 59% positive for erosions).
Conclusion:
We demonstrate the ability to utilize complete EMR data to define an RA cohort with a PPV of 94%, which was superior to an algorithm using codified data alone.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1002/acr.20184
