Journal article
Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach
R Dinga, AF Marquand, DJ Veltman, ATF Beekman, RA Schoevers, AM van Hemert, BWJH Penninx, L Schmaal
Translational Psychiatry | NATURE PUBLISHING GROUP | Published : 2018
Abstract
Many variables have been linked to different course trajectories of depression. These findings, however, are based on group comparisons with unknown translational value. This study evaluated the prognostic value of a wide range of clinical, psychological, and biological characteristics for predicting the course of depression and aimed to identify the best set of predictors. Eight hundred four unipolar depressed patients (major depressive disorder or dysthymia) patients were assessed on a set involving 81 demographic, clinical, psychological, and biological measures and were clinically followed-up for 2 years. Subjects were grouped according to (i) the presence of a depression diagnosis at 2-..
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Awarded by Geestkracht program of the Netherlands Organization for Health Research and Development (Zon-Mw)
Awarded by Neuroscience Amsterdam
Awarded by Netherlands Brain Foundation
Awarded by NWO under a VIDI fellowship
Funding Acknowledgements
This work was supported by the Geestkracht program of the Netherlands Organization for Health Research and Development (Zon-Mw, grant number 10-000-1002) and is also supported by participating universities and mental health care organizations (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen, Lentis, GGZ Friesland, GGZ Drenthe, Institute for Quality of Health Care (IQ Healthcare), Netherlands Institute for Health Services Research (NIVEL), and Netherlands Institute of Mental Health and Addiction (Trimbos). This work was also supported by Neuroscience Amsterdam (PoC-2014-NMH-02). L.S. and R.D. are supported by the Netherlands Brain Foundation grant number (F2014(1)-24). A.F.M. gratefully acknowledges support from the NWO under a VIDI fellowship (grant number 016.156.415)