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Practical foundations of machine learning for addiction research. Part I. Methods and techniques
Machine learning assembles a broad set of methods and techniques to solve a wide range of problems, such as identifying individuals with substance use disorders (SUD), finding patterns in neuroimages, understanding SUD prognostic factors and their association, or determining addiction genetic underpinnings. However, the addiction research field underuses machine learning. This two-part narrative review focuses on machine learning tools and concepts, providing an introductory insight into theirdoi:10.6084/m9.figshare.19537817.v1 fatcat:m22lml5zxfbo5akstmpq55tlcu