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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...
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. |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2016
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Materias: | |
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 |
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