Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from...

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Detalles Bibliográficos
Autores principales: Kessler, Ronald C., van Loo, Hanna M., Wardenaar, Klaas J., Bossarte, Robert M., Brenner, Lisa A., Cai, Tianxi, Ebert, David Daniel, Hwang, Irving, Li, Junlong, de Jonge, Peter, Nierenberg, Andrew A., Petukhova, Maria V., Rosellini, Anthony J., Sampson, Nancy A., Schoevers, Robert A., Wilcox, Marsha A., Zaslavsky, Alan M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4935654/
https://www.ncbi.nlm.nih.gov/pubmed/26728563
http://dx.doi.org/10.1038/mp.2015.198

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4935654/
https://www.ncbi.nlm.nih.gov/pubmed/26728563
http://dx.doi.org/10.1038/mp.2015.198

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