%0 Journal Article
%T Characterizing heterogeneity in the progression of Alzheimer's disease using longitudinal clinical and neuroimaging biomarkers
%A Bruno Giordani
%A Devendra Goyal
%A Donna Tjandra
%A Raymond Q. Migrino
%A Zeeshan Syed
%J Archive of "Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring".
%D 2018
%R 10.1016/j.dadm.2018.06.007
%X Models characterizing intermediate disease stages of Alzheimer's disease (AD) are needed to inform clinical care and prognosis. Current models, however, use only a small subset of available biomarkers, capturing only coarse changes along the complete spectrum of disease progression. We propose the use of machine learning techniques and clinical, biochemical, and neuroimaging biomarkers to characterize progression to AD
%K Machine learning
%K Trajectory
%K Longitudinal
%K Staging
%K Biomarkers
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234900/