223 citations · 251 across the 9 of their papers we have counts for
9 papers
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies
Aaron M. Smith, Tala Fakhouri, Run Zhuang +1
Applications of artificial intelligence (AI) in drug development continue to increase at a rapid pace. Regulatory authorities have provided increasingly clear perspectives on the u…
FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence
Franklin Fuller, Daniele Bertolini, Samantha Liang +2
This note introduces FRESH (Fusion of Recent Evidence and Subject Histories), a method for incorporating population-level summary results -- published clinical trials, registry sum…
Digital Twins as Synthetic Controls in Single-Arm Trials
Daniele Bertolini, Franklin Fuller, Aaron M. Smith +2
Single-arm trials are an important study design for evaluating drug efficacy and safety without enrolling patients into a control arm. Although they do not provide the gold-standar…
Digital Twin Generators for Disease Modeling
Nameyeh Alam, Jake Basilico, Daniele Bertolini +21
A patient's digital twin is a computational model that describes the evolution of their health over time. Digital twins have the potential to revolutionize medicine by enabling ind…
Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision
Yunfan Li, Jessica L. Ross, Aaron M. Smith +1
Covariate adjustment is desired by both practitioners and regulators of randomized clinical trials because it improves precision for estimating treatment effects. However, covariat…
Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins
Daniele Bertolini, Anton D. Loukianov, Aaron M. Smith +4
Alzheimer's Disease (AD) is a neurodegenerative disease that affects subjects in a broad range of severity and is assessed in clinical trials with multiple cognitive and functional…
Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
Jonathan R. Walsh, Aaron M. Smith, Yannick Pouliot +3
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 R…
Deep learning for comprehensive forecasting of Alzheimer's Disease progression
Charles K. Fisher, Aaron M. Smith, Jonathan R. Walsh +1
Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate…
Boltzmann Encoded Adversarial Machines
Charles K. Fisher, Aaron M. Smith, Jonathan R. Walsh
Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-b…