Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data

PLoS Computational Biology

Volumn 14, Issue 9, 2018, Pages

  Bhagwat, Nikhil ^a,b,c   Viviano, Joseph D ^c   Voineskos, Aristotle N ^c,d   Chakravarty, M Mallar ^a,b,e  

^a UNIVERSITY OF TORONTO   (Canada)

^b DOUGLAS MENTAL HEALTH UNIVERSITY INSTITUTE   (Canada)

^c CENTRE FOR ADDICTION AND MENTAL HEALTH   (Canada)

^d UNIVERSITY OF TORONTO   (Canada)

^e MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; FORECASTING; LEARNING SYSTEMS; MAGNETIC RESONANCE; NEUROIMAGING;

ALZHEIMER; CLINICAL SYMPTOMS; COMPUTATIONAL MODELLING; EARLY INTERVENTION; INDIVIDUAL LEVELS; LONGITUDINAL DATA; MENTAL STATE; MODELLING AND PREDICTIONS; MULTI-MODAL DATA; NEURAL-NETWORKS;

TRAJECTORIES;

ACCURACY; ALZHEIMER DISEASE; ALZHEIMER DISEASE ASSESSMENT SCALE; AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; COGNITION; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; LONGITUDINAL STUDY; MACHINE LEARNING; MINI MENTAL STATE EXAMINATION; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PREDICTION; PROGNOSIS; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; SCORING SYSTEM; SUPPORT VECTOR MACHINE; VALIDITY; AGED; AGING; ALGORITHM; AUSTRALIA; BIOLOGICAL MODEL; DISEASE EXACERBATION; FEMALE; FOLLOW UP; MALE; NERVE CELL NETWORK; PATHOPHYSIOLOGY; REPRODUCIBILITY; SOFTWARE; SYMPTOM ASSESSMENT;

BIOLOGICAL MARKER;

AGED; AGING; ALGORITHMS; ALZHEIMER DISEASE; AREA UNDER CURVE; AUSTRALIA; BIOMARKERS; DISEASE PROGRESSION; FEMALE; FOLLOW-UP STUDIES; HUMANS; LONGITUDINAL STUDIES; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MALE; MODELS, NEUROLOGICAL; NERVE NET; NEUROIMAGING; REPRODUCIBILITY OF RESULTS; SOFTWARE; SYMPTOM ASSESSMENT;

EID: 85054563739     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1006376     Document Type: Article

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