Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm
Abstract
There is a 99.6% failure rate of clinical trials for drugs to treat Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily identified at early stages. This study investigated machine learning approaches to use clinical data to predict the progression of AD in future years.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2019
- Source ID
- 10.1016/j.trci.2019.07.001
Entities
People
- Jack Albright
- The Alzheimer’s Disease Neuroimaging Initiative*
Organizations
- AbbVie
- Alzheimer's Association
- Alzheimer's Disease Neuroimaging Initiative
- Alzheimer's Drug Discovery Foundation
- BioClinica
- Biogen
- Bristol-Myers Squibb
- Canadian Institutes of Health Research
- Chiron Corporation
- Eli Lilly and Company
- GE HealthCare
- Hoffmann-La Roche
- Laboratoires Servier
- Lundbeck
- Merck & Co.
- National Institute of Biomedical Imaging and Bioengineering
- National Institute on Aging
- National Institutes of Health
- Pfizer
- Roche (United States)
- Takeda Pharmaceutical Company
- The Nueva School
- United States Department of Defense