Machine learning for comprehensive forecasting of Alzheimer’s Disease progression
Open accessAbstract
Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer’s Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer’s Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.
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Unlearn.AI has developed and tested digital twin models that leverage diverse sets of clinical data to enhance clinical trials for Alzheimer's disease and multiple sclerosis.
“From a commercial point of view, Unlearn.AI has developed and tested digital twin models that leverage diverse sets of clinical data to enhance clinical trials for Alzheimer's disease and multiple sclerosis 70,71 .”
From Multimodal biomedical AI (2022) · cites this paper as [70]
The Results (Modeling with Conditional Restricted Boltzmann Machines) describe generating synthetic patient trajectories for clinical-trial simulation, and the Abstract reports testing the model on Alzheimer’s disease data. The paper does not establish that Unlearn.AI developed the model or report a multiple-sclerosis model; its Conclusions describe clinical-trial uses as potential applications, not demonstrated improvements to trials. (LLM, conf=0.85)
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- Authors
- Fisher, C. K., Smith, A. M., Walsh, J. R., Diseases, C. A. M., Simon, A. J., Edgar, C., Jack, C. R., Holtzman, D., Russell, D., Hill, D., Grosset, D., Wood, F., Vanderstichele, H., Morris, J., Blennow, K., Marek, K., Shaw, L. M., Albert, M., Weiner, M., Fox, N., Aisen, P., Cole, P. E., Petersen, R., Sherer, T., Kubick, W., & Abbott, A. F. A. R.
- Affiliations
- Mayo Clinic (US)
- Washington University in St. Louis (US)
- Institute for Neurodegenerative Disorders (US)
- University of Glasgow (GB)
- Fujirebio (Belgium) (BE)
- Johns Hopkins University (US)
- University of California, San Francisco (US)
- University College London (GB)
- Michael J. Fox Foundation (US)
- Corresponding author
- Fisher, Charles K.
- ORCID
- Venue
- Scientific Reports
- Published
- 2019-09-20
- Volume
- 9
- Issue
- 1
- Pages
- 13622-13622
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6754403
- OpenAlex
- W2974138729
- Cited by
- 223
- References
- 66
- Citations by year
- 2026: 22 · 2025: 37 · 2024: 30 · 2023: 39 · 2022: 33 · 2021: 27 · 2020: 32 · 2019: 3
- FWCI
- 18.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease progression, mild cognitive impairment, conditional restricted Boltzmann machine, ADAS-Cog, word recall, cognitive decline prediction
- MeSH terms
- Machine Learning, Aged, Aged, 80 and over, Alzheimer Disease, Cognition, Female, Forecasting, Humans, Male, Middle Aged, Models, Theoretical, Neuropsychological Tests, Models, Statistical, Disease Progression, Cognitive Dysfunction
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