Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins
This work addresses the challenge of modeling subject outcomes across the disease spectrum for clinical trials in Alzheimer's Disease, particularly for subjects in earlier stages like Mild Cognitive Impairment.
This paper developed Digital Twins using Conditional Restricted Boltzmann Machines to model disease progression in Alzheimer's Disease and Mild Cognitive Impairment. The models were trained on observational studies and clinical trial placebo arms, effectively capturing the progression of multiple key endpoints across a broad spectrum of disease severity.
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 instruments. As clinical trials in AD increasingly focus on earlier stages of the disease, especially Mild Cognitive Impairment (MCI), the ability to model subject outcomes across the disease spectrum is extremely important. We use unsupervised machine learning models called Conditional Restricted Boltzmann Machines (CRBMs) to create Digital Twins of AD subjects. Digital Twins are simulated clinical records that share baseline data with actual subjects and comprehensively model their outcomes under standard-of-care. The CRBMs are trained on a large set of records from subjects in observational studies and the placebo arms of clinical trials across the AD spectrum. These data exhibit a challenging, but common, patchwork of measured and missing observations across subjects in the dataset, and we present a novel model architecture designed to learn effectively from it. We evaluate performance against a held-out test dataset and show how Digital Twins simultaneously capture the progression of a number of key endpoints in clinical trials across a broad spectrum of disease severity, including MCI and mild-to-moderate AD.