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 for beneficial therapies.
Digital Control Subjects for Alzheimer's Disease Clinical Trials (AMIA 2019)
November 6, 2019
The ability to simulate dozens of clinical characteristics simultaneously is a powerful tool to model disease progression. Such models have useful applications for clinical trials, from analyzing cont
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 progression of Alzheimer’s Disease.
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.
Synthetic Control Subjects for Alzheimer's Disease Clinical Trials (AAIC 2019)
July 8, 2019
Objective: 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.
Generating Synthetic Control Subjects Using Machine Learning for Clinical Trials in Alzheimer's Disease (DIA 2019)
June 25, 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 for beneficial therapies.
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.