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

This publication has 29 references indexed in Scilit:

• Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures
  Journal of Affective Disorders, 2019
• Machine Learning Approaches for Clinical Psychology and Psychiatry
  Annual Review of Clinical Psychology, 2018
• Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression
  Journal of Affective Disorders, 2018
• Pleiotropic genes in psychiatry: Calcium channels and the stress-related FKBP5 gene in antidepressant resistance
  Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2018
• Treatment resistant depression: A multi-scale, systems biology approach
  Neuroscience & Biobehavioral Reviews, 2018
• Responders to rTMS for depression show increased fronto-midline theta and theta connectivity compared to non-responders
  Brain Stimulation, 2018
• Finding treatment-resistant depression in real-world data: How a data-driven approach compares with expert-based heuristics
  Depression and Anxiety, 2017
• Heterogeneity and Homogeneity of Regional Brain Structure in Schizophrenia
  JAMA Psychiatry, 2017
• Cross-disorder risk gene CACNA1C differentially modulates susceptibility to psychiatric disorders during development and adulthood
  Molecular Psychiatry, 2017
• Genome-wide association study of antidepressant response: involvement of the inorganic cation transmembrane transporter activity pathway
  BMC Psychiatry, 2016