Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks

Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by a complex set of clinical assessments. We use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to learn the relationships between covariates commonly used to characterize subjects and their disease progression in MS clinical trials. A CRBM is capable of generating digital twins, which are simulated subjects having the same baseline data as actual subjects. Digital twins allow for subject-level statistical analyses of disease progression. The CRBM is trained using data from 2395 subjects enrolled in the placebo arms of clinical trials across the three primary subtypes of MS. We discuss how CRBMs are trained and show that digital twins generated by the model are statistically indistinguishable from their actual subject counterparts along a number of measures.

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Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins


Bayesian prognostic covariate adjustment


Using Digital Twins to Decrease Enrollment and Increase Statistical Power in Alzheimer's Disease Trials (CTAD 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.
Our novel method - Bayesian prognostic covariate adjustment - is a Bayesian analysis that draws on the strengths of the prognostic model approach.
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.