Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
Walsh, J. R.; Smith, A. M.; Pouliot, Y.; Li-Bland, D.; Loukianov, A.; Fisher, C. K.; Multiple Sclerosis Outcome Assessments Consortium,
Show 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease 94%
- Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments 94%
- A time-series analysis of blood-based biomarkers within a 25-year longitudinal dolphin cohort. 94%
Similar papers in this journal
- Generation of realistic synthetic data using multimodal neural ordinary differential equations 95%
- Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis 95%
- Continuous-Time and Dynamic Suicide Attempt Risk Prediction with Neural Ordinary Differential Equations 94%
Similar papers in this journal
- Compressive Big Data Analytics: An Ensemble Meta-Algorithm for High-dimensional Multisource Datasets 94%
- Towards development of a statistical framework to evaluate myotonic dystrophy type 1 mRNA biomarkers in the context of a clinical trial 93%
- Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement 93%
Similar papers in this journal
- Mitigating Machine Learning Bias Between High Income and Low-Middle Income Countries for Enhanced Model Fairness and Generalizability 93%
- Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy 93%
- Accounting for endogenous effects in decision-making with a non-linear diffusion decision model 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.