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dc.contributor.authorSzolovits, Peter
dc.contributor.authorLiao, Katherine P.
dc.contributor.authorCai, Tianxi
dc.contributor.authorGainer, Vivian
dc.contributor.authorGoryachev, Sergey
dc.contributor.authorZeng-Treitler, Qing
dc.contributor.authorRaychaudhuri, Soumya
dc.contributor.authorChurchill, Susanne
dc.contributor.authorMurphy, Shawn N.
dc.contributor.authorKohane, Isaac
dc.contributor.authorKarlson, Elizabeth W.
dc.contributor.authorPlenge, Robert M.
dc.date.accessioned2012-11-26T20:18:51Z
dc.date.available2012-11-26T20:18:51Z
dc.date.issued2010-03
dc.date.submitted2010-03
dc.identifier.issn0004-3591
dc.identifier.issn1529-0131
dc.identifier.urihttp://hdl.handle.net/1721.1/75028
dc.description.abstractObjective: 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.en_US
dc.description.sponsorshipNational Library of Medicine (U.S.) (Award U54LM008748)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.). i2b2 (Informatics for Integrating Biology and the Bedside) (Grant U54-LM008748)en_US
dc.language.isoen_US
dc.publisherWiley Blackwell (John Wiley & Sons)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1002/acr.20184en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourcePubMed Centralen_US
dc.titleElectronic Medical Records for Discovery Research in Rheumatoid Arthritisen_US
dc.typeArticleen_US
dc.identifier.citationLiao, Katherine P. et al. “Electronic Medical Records for Discovery Research in Rheumatoid Arthritis.” Arthritis Care & Research 62.8 (2010): 1120–1127.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorSzolovits, Peter
dc.relation.journalArthritis Care & Researchen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsLiao, Katherine P.; Cai, Tianxi; Gainer, Vivian; Goryachev, Sergey; Zeng-treitler, Qing; Raychaudhuri, Soumya; Szolovits, Peter; Churchill, Susanne; Murphy, Shawn; Kohane, Isaac; Karlson, Elizabeth W.; Plenge, Robert M.en
dc.identifier.orcidhttps://orcid.org/0000-0001-8411-6403
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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