Cargando…
Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach
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 predictin...
Autores principales: | Dinga, Richard, Marquand, Andre F., Veltman, Dick J., Beekman, Aartjan T. F., Schoevers, Robert A., van Hemert, Albert M., Penninx, Brenda W. J. H., Schmaal, Lianne |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218451/ https://www.ncbi.nlm.nih.gov/pubmed/30397196 http://dx.doi.org/10.1038/s41398-018-0289-1 |
Ejemplares similares
-
Predicting the Naturalistic Course of Major Depressive Disorder Using Clinical and Multimodal Neuroimaging Information: A Multivariate Pattern Recognition Study
por: Schmaal, Lianne, et al.
Publicado: (2015) -
Evaluating the evidence for biotypes of depression: Methodological replication and extension of Drysdale et al. (2017)
por: Dinga, Richard, et al.
Publicado: (2019) -
Predicting individual clinical trajectories of depression with generative embedding
por: Frässle, Stefan, et al.
Publicado: (2020) -
The role of anxious distress in immune dysregulation in patients with major depressive disorder
por: Gaspersz, Roxanne, et al.
Publicado: (2017) -
Reconsidering the prognosis of major depressive disorder across diagnostic boundaries: full recovery is the exception rather than the rule
por: Verduijn, Judith, et al.
Publicado: (2017)