Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach.

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-year follow-up (yes n = 397, no n = 407), and (ii) three disease course trajectory groups (rapid rem

SUBMITTER: Dinga R 

PROVIDER: S-EPMC6218451 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature