Characterizing the clinical heterogeneity of early symptomatic Alzheimer’s disease: a data-driven machine learning approach
Our findings show usefulness in the stratification of patients in early AD and may thus increase the chances of finding a treatment for future AD clinical trials.
Authors
- Xiwu Wang (ORCID: https://orcid.org/0009-0005-8291-4141)
- Jie Zhang (ORCID: https://orcid.org/0009-0002-6551-5921)
- Teng Ye
- Wenjun Zhou (ORCID: https://orcid.org/0009-0009-1865-7514)
- Deguo Jiang
Institutions
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Wenzhou City People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2024-08-12
- DOI
- https://doi.org/10.3389/fnagi.2024.1410544
- Citations
- 4
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 1.54
Funders
- U.S. Department of Defense
- Alzheimer's Association
- Bristol-Myers Squibb
- Eli Lilly and Company
- Pfizer
- Biogen
- University of Southern California
- Meso Scale Diagnostics
- Alzheimer's Disease Neuroimaging Initiative
- BioClinica
- Novartis Pharmaceuticals Corporation
- Northern California Institute for Research and Education
- Eisai
- Servier
- H. Lundbeck A/S
- IXICO
- National Institutes of Health
- Genentech
- Canadian Institutes of Health Research
- DoD Alzheimer's Disease Neuroimaging Initiative