Generating Synthetic Control Subjects Using Machine Learning for Clinical Trials in Alzheimer's Disease (DIA 2019)

Objective: To develop a method to model disease progression that simulates detailed patient trajectories. To apply this model to subjects in control arms of Alzheimer's disease clinical trials. Methods: We used a robust data processing framework to build a machine learning dataset from a database of subjects in the control arms of a diverse set of 28 different clinical trials on Alzheimer's disease. From this dataset, we selected 1908 subjects with 18-month trajectories of 44 variables and trained 5 cross-validated models capable of simulating disease progression in 3-month intervals across all variables. Results: Based on a statistical analysis comparing data from actual patients with simulated patients, the model generates accurate patient-level distributions across variables and through time. Focusing on a common clinical trial endpoint for Alzheimer's disease (ADAS-Cog), we show the model can predict disease progression as accurately as several supervised models. Our model also predicts the outcome of a clinical trial whose data are distinct from the training and test datasets. Conclusion: The ability to simulate dozens of patient characteristics simultaneously is a powerful tool to model disease progression. Such models have useful applications for clinical trials, from analyzing control groups to supplementing real subject data in control arms.

Enter your email address to download paper.

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

Embracing Innovation to Move Forward

Podcasts

Your Digital Twin - UnlearnAI

Podcasts

Using AI Digital Twins for Drug Testing

Dr. Charles Fisher, CEO of Unlearn AI, discusses creating digital clones by using artificial intelligence for use in clinical drug trials.
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.
At Unlearn, our goal is to use the data available from historical trials, to generate new evidence to inform and advance research.