Cross-trial prediction of treatment outcome in depression: a machine learning approach.

Journal: The lancet. Psychiatry
PMID: 26803397

Abstract

BACKGROUND: Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram.

Authors

  • Adam Mourad Chekroud
    Department of Psychology, Yale University, New Haven, CT, USA; Centre for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA. Electronic address: adam.chekroud@yale.edu.
  • Ryan Joseph Zotti
    Capital One, McLean, VA, USA.
  • Zarrar Shehzad
    Department of Psychology, Yale University, New Haven, CT, USA.
  • Ralitza Gueorguieva
    Department of Biostatistics, Yale University, New Haven, CT, USA.
  • Marcia K Johnson
    Department of Psychology, Yale University, New Haven, CT, USA.
  • Madhukar H Trivedi
    Department of Psychiatry, UT Southwestern, Dallas, TX, USA.
  • Tyrone D Cannon
    Department of Psychology, Yale University, New Haven, CT, United States.
  • John Harrison Krystal
    Department of Psychiatry, Yale University, New Haven, CT, USA.
  • Philip Robert Corlett
    Department of Psychiatry, Yale University, New Haven, CT, USA.

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