Can Machine Learning help us in dealing with treatment resistant depression? A review.

Journal: Journal of affective disorders
PMID: 31437696

Abstract

BACKGROUND: About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomatic even after multiple trials are applied, configuring a state called treatment resistant depression (TRD). A clear definition of this state and the understanding of underlying mechanisms contributing to chronic disability caused by major depressive disorder is still unknown. Therefore, Machine Learning (ML) techniques emerged in the last years as interesting approaches to deal with such complex problems.

Authors

  • Alessandro Pigoni
    Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, Milan, Italy; University of Milan, Department of Pathophysiology and Transplantation, Milan, Italy.
  • Giuseppe Delvecchio
    University of Milan, Department of Pathophysiology and Transplantation, Milan, Italy.
  • Domenico Madonna
    Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, Milan, Italy; University of Milan, Department of Pathophysiology and Transplantation, Milan, Italy.
  • Cinzia Bressi
    Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, Milan, Italy; University of Milan, Department of Pathophysiology and Transplantation, Milan, Italy.
  • Jair Soares
    Department of Psychiatry and Behavioural Sciences, UT Houston Medical School, Houston, TX, USA.
  • Paolo Brambilla
    Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy; Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, TX, USA. Electronic address: paolo.brambilla1@unimi.it.

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