Generating Digital Control Subjects using Machine Learning for Alzheimer's Disease Clinical Trials (CTAD 2019)

Background: Recently, there has been a flurry of attention focused on the benefits of synthetic control patients in clinical trials. 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. Objective: To demonstrate a machine learning model is capable of simulating Alzheimer's Disease progression and generate digital control subjects that are statistically indistinguishable from actual controls. Methods: We developed a machine learning model of Alzheimer's Disease progression trained with data from 4897 subjects from 28 clinical trial control arms involving early or moderate Alzheimer's Disease. The model is an example of a Conditional Restricted Boltzmann Machine (CRBM), a kind of undirected neural network whose properties are well suited to the task of modeling clinical data progression. The model generates values for 47 variables for each digital control subject at three-month intervals. Results: Based on a statistical analysis comparing data from actual and digital control subjects, the model generates accurate subject-level distributions across variables and through time that are statistically indistinguishable from actual data. Conclusion: Our work demonstrates the potential for the CRBMs to generate digital control subjects that are statistically indistinguishable from actual control subjects, with promising applications for Alzheimer's Disease clinical trials.

Enter your email address to download paper.

Click the link to begin download.
Oops! Something went wrong while submitting the form.

Enter your email address to watch the webinar.

Click the link to watch webinar.
Oops! Something went wrong while submitting the form.
Webinars

Why can’t we agree on how to define digital twins in healthcare?

White Papers

Summary of the EMA September 2022 Qualification Opinion for PROCOVA™

Press

Charles Fisher, Unlearn.AI: “now is the time to adopt AI-based solutions”

The potential for AI implementation in healthcare can barely be measured, as it can already do what humans do, just countless times better and more efficiently.
The European Medicines Agency has qualified Unlearn’s AI-powered method for running smaller, faster clinical trials.
Digital twins seem to be everywhere in healthcare now, but no one agrees on a single definition for them.