University of Worcester Worcester Research and Publications
 
  USER PANEL:
  ABOUT THE COLLECTION:
  CONTACT DETAILS:

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Castro, V.M. and Minnier, J. and Murphy, S.N. and Kohane, I. and Churchill, S.E. and Gainer, V. and Cai, T. and Hoffnagle, A.G. and Dai, Y. and Block, S. and Weill, S.R. and Nadal-Vicens, M. and Pollastri, A.R. and Rosenquist, J.N. and Goryachev, S. and Ongur, D. and Sklar, P. and Perlis, R.H. and Smoller, J.W. and Lee, P.H. and Stahl, E.A. and Purcell, S.M. and Ruderfer, D.M. and Charney, A.W. and Roussos, P. and Pato, C. and Pato, M. and Medeiros, H. and Sobel, J. and Craddock, N. and Jones, I. and Forty, L. and DiFlorio, A. and Green, E. and Jones, Lisa and Dunjewski, K. and Landén, M. and Hultman, C. and Juréus, A. and Bergen, S. and Svantesson, O. and McCarroll, S. and Moran, J. and Chambert, R.A. and Belliveau, R.A. (2014) Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls. The American Journal of Psychiatry, 172 (4). pp. 363-372. ISSN Print: 0002-953X Online: 1535-7228

Full text not available from this repository. (Request a copy)

Abstract

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.

Item Type: Article
Additional Information:

The full-text can be accessed via the official URL.

Uncontrolled Keywords: Bipolar disorder, electronic health record, phenotyping
Subjects: B Philosophy. Psychology. Religion > BF Psychology
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Academic Departments > Institute of Health and Society
Related URLs:
Depositing User: Lisa Jones
Date Deposited: 13 Oct 2016 11:12
Last Modified: 13 Oct 2016 11:12
URI: https://eprints.worc.ac.uk/id/eprint/4972

Actions (login required)

View Item View Item
 
     
Worcester Research and Publications is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.