Identifying incipient dementia individuals using machine learning and amyloid imaging

Neurobiology of Aging

Volumn 59, Issue , 2017, Pages 80-90

  Mathotaarachchi, Sulantha ^a,b   Pascoal, Tharick A ^a   Shin, Monica ^a   Benedet, Andrea L ^a   Kang, Min Su ^a   Beaudry, Thomas ^a   Fonov, Vladimir S ^b   Gauthier, Serge ^a,c   Rosa Neto, Pedro ^a,b,c  

^a MCGILL UNIVERSITY   (Canada)

^b MCGILL UNIVERSITY   (Canada)

^c MCGILL UNIVERSITY   (Canada)

Author keywords

Alzheimer's disease; Amyloid; Mild cognitive impairment; Prediction; Random forest; Random under sampling

Indexed keywords

AGED; ALGORITHM; ALZHEIMER DISEASE; ARTICLE; CONTROLLED STUDY; DEMENTIA; DEMENTIA ASSESSMENT; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE COURSE; FEMALE; HUMAN; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MILD COGNITIVE IMPAIRMENT; POSITRON EMISSION TOMOGRAPHY; PRIORITY JOURNAL; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; COGNITIVE DEFECT; DIAGNOSTIC IMAGING; DISEASE EXACERBATION; MIDDLE AGED; PREDICTIVE VALUE; PROBABILITY; TIME FACTOR; VERY ELDERLY;

BIOLOGICAL MARKER;

AGED; AGED, 80 AND OVER; ALZHEIMER DISEASE; BIOMARKERS; COGNITIVE DYSFUNCTION; DISEASE PROGRESSION; FEMALE; HUMANS; MACHINE LEARNING; MALE; MIDDLE AGED; POSITRON-EMISSION TOMOGRAPHY; PREDICTIVE VALUE OF TESTS; PROBABILITY; TIME FACTORS;

EID: 85026316928     PISSN: 01974580     EISSN: 15581497     Source Type: Journal    
DOI: 10.1016/j.neurobiolaging.2017.06.027     Document Type: Article

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