How are we changing clinical trials?

How are we changing clinical trials?

How are we changing clinical trials?

Publications

Boltzmann Encoded Adversarial Machines

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.
Publications

Bayesian prognostic covariate adjustment

Publications

Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score

Blog

Uncertainty is More Important to AI than Explainability

Uncertainty is an alternative to explainability that places no constraints on the complexity of the underlying algorithm.
Linear adjustment for a prognostic score is an effective and safe method for leveraging historical data to reduce uncertainty in randomized trials.
Our novel method - Bayesian prognostic covariate adjustment - is a Bayesian analysis that draws on the strengths of the prognostic model approach.

Machine learning for comprehensive forecasting of Alzheimer's Disease progression

Don't Model Humans with SNPs

Publications

Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks

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.
Blog

The Travails of Comparing Generative Models

Blog

Introducing Paysage

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Paysage is a powerful library for training RBMs — and more-generally, energy-based neural network models.
Increasingly, the task of machine learning will be to explore the formidable frontier of unsupervised learning.

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