Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review
The functional and psychosocial deficits associated with depression are pervasive, often chronic, progressive, and highly disabling (Vigo et al., 2016). The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial reported that patients who remit with the first trial of an antidepressant experience significant reductions in work-related disability; in contrast, patients who remit with subsequent treatment trials or strateg ies exhibit residual functional impairments (Trivedi et al., 2013).
Source: Journal of Affective Disorders - Category: Neurology Authors: Yena Lee, Renee-Marie Ragguett, Rodrigo B. Mansur, Justin J. Boutilier, Joshua D. Rosenblat, Alisson Trevizol, Elisa Brietzke, Kangguang Lin, Zihang Pan, Mehala Subramaniapillai, Timothy C.Y. Chan, Dominika Fus, Caroline Park, Natalie Musial, Hannah Zucke Tags: Review article Source Type: research