Refining Prediction in Treatment-Resistant Depression: Results of Machine Learning Analyses in the TRD III Sample.

Journal: The Journal of clinical psychiatry
PMID: 29228516

Abstract

OBJECTIVE: The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large set of 47 clinical and sociodemographic predictors of treatment outcome.

Authors

  • Alexander Kautzky
    Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
  • Markus Dold
    Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
  • Lucie Bartova
    Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
  • Marie Spies
    Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
  • Thomas Vanicek
    Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
  • Daniel Souery
    Université Libre de Bruxelles and Psy Pluriel Centre Europèen de Psychologie Medicale, Brussels, Belgium.
  • Stuart Montgomery
    Imperial College, University of London, London, United Kingdom.
  • Julien Mendlewicz
    School of Medicine, Free University of Brussels, Brussels, Belgium.
  • Joseph Zohar
    Psychiatric Division, Chaim Sheba Medical Center, Ramat Gan, Israel.
  • Chiara Fabbri
    Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
  • Alessandro Serretti
    Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
  • Rupert Lanzenberger
    Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
  • Siegfried Kasper
    Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20, A-1090 Vienna, Austria. sci-biolpsy@meduniwien.ac.at.

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