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Validation of electronic health record phenotyping of bipolar disorder cases and controls.

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Validation of electronic health record phenotyping of bipolar disorder cases and controls.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Castro, Victor M. Minnier, Jessica Murphy, Shawn N. Kohane, Isaac Churchill, Susanne E. Gainer, Vivian Cai, Tianxi Hoffnagle, Alison G. Dai, Yael Block, Stefanie Weill, Sydney R. Nadal-Vicens, Mireya Pollastri, Alisha R. Rosenquist, J. Niels Goryachev, Sergey Ongur, Dost Sklar, Pamela Perlis, Roy H. Smoller, Jordan W.
Organization	International Cohort Collection for Bipolar Disorder Consortium
Description	Author Affiliation: Castro VM ( From Research Information Systems and Computing, Partners HealthCare System, Boston)
Abstract	OBJECTIVE: The study was designed to validate use of electronic 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 diagnoses was calculated against diagnoses from direct semistructured 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 disorder on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based classifications 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.
File Format	HTM / HTML
ISSN	0002953X
e-ISSN	15357228
DOI	10.1176/appi.ajp.2014.14030423
Journal	American Journal of Psychiatry
Issue Number	4
Volume Number	172
Language	English
Publisher	American Psychiatric Association
Publisher Date	2015-04-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Discipline Psychiatry Bipolar Disorder Diagnosis Electronic Health Records Natural Language Processing Algorithms Classification Psychology Case-control Studies Cohort Studies Phenotype Predictive Value Of Tests Reproducibility Of Results Sensitivity And Specificity Research Support, N.i.h., Extramural Validation Studies
Content Type	Text
Resource Type	Article
Subject	Psychiatry and Mental Health

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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