Machine learning for comprehensive forecasting of Alzheimer's Disease progression
September 20, 2019
We have shown that generative models capable of sampling conditional probability distributions over a diverse array of clinical variables can accurately model the progression of Alzheimer’s Disease.
Broadening Role for External Control Arms in Clinical Trials
July 12, 2019
Unlearn.AI Inc. is going a step further, using data from historical trials and patient registries to build algorithms that simulate artificial patients.
The ability to reduce the burden on control subjects with subjects in clinical trials for complex diseases like Alzheimer’s Disease would drastically improve the search for beneficial therapies.
The San Francisco-based company creates digital replicas of participants in clinical drug trials using artificial intelligence. The result, says Charles Fisher...
A fascinating approach to the problem of how to make clinical trials more efficient, and understand more about what may be possible with more and better patient data.
Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
February 5, 2020
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.
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.
