Improving antidepressant outcomes: what works for whom and why?

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Thalia Eley and Gerome Breen explore a new systematic meta-review of predictors of antidepressant treatment outcome in depression, which looks at clinical and demographic variables, but also biomarkers including both genetic and neuroimaging data.

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Predicting suicide attempts in adolescents: machine learning is powerful, but don’t forget Bayes’ rule

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Derek de Beurs explores a recent study that uses longitudinal clinical data and machine learning to predict suicide attempts in adolescents.

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Trajectories of depressive symptoms in children and adolescents

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Jess Bone on a systematic review of longitudinal studies, which explores the different trajectories of depressive symptoms in children and adolescents, and the factors that might help predict or protect young people.

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Can a machine learning approach help us predict what specific treatments work best for individuals with depression?

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Marcus Munafo explores a recent study that uses a machine learning approach across two trials (STARD*D and CO-MED) to try and predict treatment outcomes (primarily focusing on the antidepressant citalopram) for depression.

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