Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins
December 24, 2020
Here, we have demonstrated that a particular type of generative model (i.e., CRBMs) can be used to accurately model disease progression for patients with MCI or AD.
Using Digital Twins to Decrease Enrollment and Increase Statistical Power in Alzheimer's Disease Trials (CTAD 2020)
November 4, 2020
We showed that digital twins could reduce the number of control subjects required in the analysis to achieve equivalent results to an analysis of the actual subjects.
Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
February 5, 2020
Using a dataset of subjects enrolled in the placebo arms of MS clinical trials, we trained a Conditional Restricted Boltzmann Machine to generate digital subjects.
Generating Digital Control Subjects using Machine Learning for Alzheimer's Disease Clinical Trials (CTAD 2019)
December 6, 2019
The ability to reduce the burden on control subjects with subjects in clinical trials for complex diseases like Alzheimer’s Disease would drastically improve the search..
Machine learning for comprehensive forecasting of Alzheimer's Disease progression
September 20, 2019
We have shown that generative models capable of sampling conditional probability distributions over a diverse array of clinical variables can accurately model the...
Synthetic Control Subjects for Alzheimer's Disease Clinical Trials (JSM 2019)
July 25, 2019
To develop a method to model disease progression that simulates detailed clinical data records for subjects in the control arms of Alzheimer's disease clinical trials.
The deep learning revolution has driven tremendous advances on supervised learning problems, and a primary outcome is that feed-forward neural networks have become a powerful tool.