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Volumn 145, Issue , 2017, Pages 254-264

Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning

Author keywords

Big data; Bipolar disorder; Heterogeneity; Machine learning; Neuroimaging; Neuropsychology; Research Domain Criteria (RDoC)

Indexed keywords

ACCURACY; ADULT; ARTICLE; BIPOLAR DISORDER; CONTROLLED STUDY; EXECUTIVE FUNCTION; FEMALE; FRACTIONAL ANISOTROPY; HUMAN; IMAGE PROCESSING; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MENTAL TASK; NEUROIMAGING; NEUROPHYSIOLOGY; OCCIPITOFRONTAL FASCICULUS; PHENOTYPE; PREDICTION; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; WHITE MATTER; DIAGNOSTIC IMAGING; DIFFUSION WEIGHTED IMAGING; MIDDLE AGED; PATHOPHYSIOLOGY; PROCEDURES;

EID: 84975736125     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2016.02.016     Document Type: Article
Times cited : (68)

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