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Machine learning applications in clinical neurosciences have emerged as a powerful, yet controversial approach to achieving highly personalized health care. Studies conducted in the fields of oncology, cardiology and other select disciplines provide foundational support for the utility of machine learning to facilitate the discovery of novel disease mechanisms, targets for therapeutic interventions and predictive models of individual outcomes. By comparison, the application of machine learning in clinical neuroscience has moved at a glacial pace. This presentation will describe the potential for machine learning to resolve longstanding knowledge gaps related to the most vexing neurologic and psychiatric conditions, as well as key challenges and methodologic/interpretative risks and recommendations for best practices. Outcomes from studies using ensemble machine learning will be emphasized.

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