Characterizing heterogeneity in the progression of Alzheimer's disease using longitudinal clinical and neuroimaging biomarkers
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2018
- Source ID
- 10.1016/j.dadm.2018.06.007
Entities
People
- Bruno Giordani
- Devendra Goyal
- Donna Tjandra
- Jenna Wiens
- Raymond Q. Migrino
- The Alzheimer’s Disease Neuroimaging Initiative*
- Zeeshan Syed
Organizations
- National Institutes of Health
- National Science Foundation
- Stanford University
- United States Department of Defense
- University of Michigan