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      Machine Learning for Precision Psychiatry: Opportunities and Challenges.

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          Abstract

          The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet psychiatrists and investigators now have an unprecedented opportunity to benefit from complex patterns in brain, behavior, and genes using methods from machine learning (e.g., support vector machines, modern neural-network algorithms, cross-validation procedures). Combining these analysis techniques with a wealth of data from consortia and repositories has the potential to advance a biologically grounded redefinition of major psychiatric disorders. Increasing evidence suggests that data-derived subgroups of psychiatric patients can better predict treatment outcomes than DSM/ICD diagnoses can. In a new era of evidence-based psychiatry tailored to single patients, objectively measurable endophenotypes could allow for early disease detection, individualized treatment selection, and dosage adjustment to reduce the burden of disease. This primer aims to introduce clinicians and researchers to the opportunities and challenges in bringing machine intelligence into psychiatric practice.

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          Author and article information

          Journal
          Biol Psychiatry Cogn Neurosci Neuroimaging
          Biological psychiatry. Cognitive neuroscience and neuroimaging
          Elsevier BV
          2451-9030
          2451-9022
          Mar 2018
          : 3
          : 3
          Affiliations
          [1 ] Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, Aachen, Germany; Parietal team, INRIA, Neurospin, Gif-sur-Yvette, France. Electronic address: danilo.bzdok@rwth-aachen.de.
          [2 ] Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
          Article
          S2451-9022(17)30206-9
          10.1016/j.bpsc.2017.11.007
          29486863
          d8c2d0a2-33db-4f43-831c-e7b0b08da70e
          History

          Research Domain Criteria (RDoC),Artificial intelligence,Endophenotypes,Machine learning,Null-hypothesis testing,Personalized medicine,Predictive analytics,Single-subject prediction

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