  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.
