Major depressive disorder subtypes to predict long-term course

BACKGROUND: Variation in course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. METHODS: Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs) followed by k-me...

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Main Authors: van Loo, Hanna M., Cai, Tianxi, Gruber, Michael J., Li, Junlong, de Jonge, Peter, Petukhova, Maria, Rose, Sherri, Sampson, Nancy A., Schoevers, Robert A., Wardenaar, Klaas J., Wilcox, Marsha A., Al-Hamzawi, Ali Obaid, Andrade, Laura Helena, Bromet, Evelyn J., Bunting, Brendan, Fayyad, John, Florescu, Silvia E., Gureje, Oye, Hu, Chiyi, Huang, Yueqin, Levinson, Daphna, Medina-Mora, Maria Elena, Nakane, Yoshibumi, Posada-Villa, Jose, Scott, Kate M., Xavier, Miguel, Zarkov, Zahari, Kessler, Ronald C.
Formáid: Artigo
Teanga:Inglês
Foilsithe: 2014
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Article
Rochtain Ar Líne:https://ncbi.nlm.nih.gov/pmc/articles/PMC5125445/
https://ncbi.nlm.nih.gov/pubmed/24425049
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/da.22233
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