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Alzheimer's Disease (AD) is a neurodegenerative disease that affects subjectsin a broad range of severity and is assessed in clinical trials with multiplecognitive and functional instruments. As clinical trials in AD increasinglyfocus on earlier stages of the disease, especially Mild Cognitive Impairment(MCI), the ability to model subject outcomes across the disease spectrum isextremely important. We use unsupervised machine learning models calledConditional Restricted Boltzmann Machines (CRBMs) to create Digital Twins of ADsubjects. Digital Twins are simulated clinical records that share baseline datawith actual subjects and comprehensively model their outcomes understandard-of-care. The CRBMs are trained on a large set of records from subjectsin observational studies and the placebo arms of clinical trials across the ADspectrum. These data exhibit a challenging, but common, patchwork of measuredand missing observations across subjects in the dataset, and we present a novelmodel architecture designed to learn effectively from it. We evaluateperformance against a held-out test dataset and show how Digital Twinssimultaneously capture the progression of a number of key endpoints in clinicaltrials across a broad spectrum of disease severity, including MCI andmild-to-moderate AD.

Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins