Machine learning for comprehensive forecasting of Alzheimer's Disease progression

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 towardspersonalized 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 MildCognitive 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.

Enter your email address to download paper.

Click the link to begin download.
Oops! Something went wrong while submitting the form.
White Papers

Applications of Digital Twins in Clinical Trials for Alzheimer’s Disease

Press

Unlearn.AI named to the 2021 CB Insights AI 100 List of Most Innovative Artificial Intelligence Startups

Blog

Welcoming Dr. Taylor to Unlearn.AI’s Board of Directors

I’m honored to have such an inspirational and experienced leader like Dr. Taylor join our board.
The AI 100 is CB Insights' annual list of the 100 most promising private AI companies in the world.
How Digital Twins make it possible to design and run more efficient clinical trials with well-defined statistical properties.