Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks

• URL: http://arxiv.org/abs/2002.02779v2
• Date: Sun, 19 Apr 2020 17:39:08 GMT
• Title: Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
• Authors: Jonathan R. Walsh, Aaron M. Smith, Yannick Pouliot, David Li-Bland, Anton Loukianov, and Charles K. Fisher
• Abstract summary: Digital twins are simulated subjects having the same baseline data as actual subjects. We show that digital twins generated by the model are statistically indistinguishable from their actual subject counterparts along a number of measures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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