Matthew Broome considers a Finnish study on the potential of predicting psychosis and bipolar disorder in young people who have previously used child and adolescent mental health services.
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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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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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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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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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