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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Part 3: Innovation in Clinical Research: AI-based Drug Development Tools and the Regulatory Landscape‍

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Part 2: Faster, More Efficient Trials: Novel Trial Designs using Digital Twins‍

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Part 1: AI, Digital Twins, and the Future of Clinical Research‍

Learn about how Digital Twins are created and how they are incorporated into clinical trials to increase power, accelerate timelines, and enable patient level insights.
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