Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk
Abstract
Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predictors automatically. To date, there is no empirical research comparing the prognostic accuracy of these two methods for the prediction of psychosis onset. In a first experiment, no improved performance...
Paper Details
Title
Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk
Published Date
Oct 17, 2019
Journal
Volume
9
Issue
1
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