Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm
Open accessAbstract
Introduction: 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. Methods: Data from 1737 patients were processed using the “All‐Pairs” technique, a novel methodology created for this study involving the comparison of all possible pairs of temporal data points for each patient. Machine learning models were trained on these processed data and evaluated using a separate testing data set (110 patients). Results: A neural network model was effective (mAUC = 0.866) at predicting the progression of AD, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. Discussion: Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.
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Albright (2019) predicted progression of Alzheimer's using an All-Pairs technique and a neural network model showed mAUC = 0.866 in normal and mild cognitive impairment diagnosed patients.
“Albright (2019) predicted the progression of Alzheimers using “All-Pairs” technique with data of 1737 patients, comparing possible pairs of temporal data points. When a separate data set (110 patients) was used for machine learning models, a neural network model showed good result (mAUC = 0.866) at predicting the progression of Alzheimer in normal and mild cognitive impairment diagnosed patients [”
From Recent Advancements in Pathogenesis, Diagnostics and Treatment of Alzheimer’s Disease (2020) · cites this paper as [143]
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References 31 · Cited by 104
- Authors
- Albright, J., & Initiative, A. D. N.
- Affiliations
- The Nueva School San Mateo CA
- Corresponding author
- Albright, Jack
- Venue
- Alzheimer's & Dementia: Translational Research & Clinical Interventions
- Publisher
- Wiley
- Published
- 2019-01
- Volume
- 5
- Issue
- 1
- Pages
- 483-491
- ISSN
- 2352-8737 (ISSN-L)
- PMC
- PMC6804703
- OpenAlex
- W2921792886
- Cited by
- 104
- References
- 31
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 15 · 2023: 19 · 2022: 19 · 2021: 22 · 2020: 5 · 2019: 2
- FWCI
- 9.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Alzheimer s & Dementia Translational Research & Clinical Interventions (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Alzheimer's Disease Neuroimaging Initiative
- National Institutes of Health (AG024904)
- U.S. Department of Defense
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- BioClinica
- Biogen
- Bristol-Myers Squibb
- Eli Lilly and Company
- Roche
- Genentech
- Fujirebio US
- GE Healthcare
- H. Lundbeck A/S
- Merck
- Novartis Pharmaceuticals Corporation
- Pfizer
- Servier
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- Alzheimer's disease, disease progression prediction, early detection, mild cognitive impairment, temporal data, artificial neural networks, clinical trial failure
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