Using electronic medical records to enable large-scale studies in psychiatry: Treatment resistant depression as a model

R. H. Perlis, D. V. Iosifescu, V. M. Castro, S. N. Murphy, V. S. Gainer, J. Minnier, T. Cai, S. Goryachev, Q. Zeng, P. J. Gallagher, M. Fava, J. B. Weilburg, S. E. Churchill, I. S. Kohane, J. W. Smoller

Research output: Contribution to journalReview articlepeer-review

124 Scopus citations


Background Electronic medical records (EMR) provide a unique opportunity for efficient, large-scale clinical investigation in psychiatry. However, such studies will require development of tools to define treatment outcome.Method Natural language processing (NLP) was applied to classify notes from 127 504 patients with a billing diagnosis of major depressive disorder, drawn from out-patient psychiatry practices affiliated with multiple, large New England hospitals. Classifications were compared with results using billing data (ICD-9 codes) alone and to a clinical gold standard based on chart review by a panel of senior clinicians. These cross-sectional classifications were then used to define longitudinal treatment outcomes, which were compared with a clinician-rated gold standard.Results Models incorporating NLP were superior to those relying on billing data alone for classifying current mood state (area under receiver operating characteristic curve of 0.85-0.88 v. 0.54-0.55). When these cross-sectional visits were integrated to define longitudinal outcomes and incorporate treatment data, 15% of the cohort remitted with a single antidepressant treatment, while 13% were identified as failing to remit despite at least two antidepressant trials. Non-remitting patients were more likely to be non-Caucasian (p<0.001).Conclusions The application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations. Continued development will be required to better address moderators of outcome such as adherence and co-morbidity.

Original languageEnglish (US)
Pages (from-to)41-50
Number of pages10
JournalPsychological Medicine
Issue number1
StatePublished - Jan 2012
Externally publishedYes


  • Antidepressant
  • classification
  • machine learning
  • natural language processing
  • remission
  • treatment resistant depression

ASJC Scopus subject areas

  • Applied Psychology
  • Psychiatry and Mental health


Dive into the research topics of 'Using electronic medical records to enable large-scale studies in psychiatry: Treatment resistant depression as a model'. Together they form a unique fingerprint.

Cite this