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A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder.

08:00 EDT 18th September 2018 | BioPortfolio

Summary of "A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder."

The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools.

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Journal Details

This article was published in the following journal.

Name: Journal of affective disorders
ISSN: 1573-2517
Pages: 503-515

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