Title: Deep learning for comprehensive forecasting of Alzheimer's Disease progression,
Abstract: 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 detailed patient trajectories. We use
data comprising 18-month trajectories of 44 clinical variables from 1908
patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model
for personalized forecasting of disease progression. We simulate synthetic
patient data including the evolution of each sub-component of cognitive exams,
laboratory tests, and their associations with baseline clinical
characteristics, generating both predictions and their confidence intervals.
Our unsupervised model predicts changes in total ADAS-Cog scores with the same
accuracy as specifically trained supervised models and identifies
sub-components associated with word recall as predictive of progression. The
ability to simultaneously simulate dozens of patient characteristics is a
crucial step towards personalized medicine for Alzheimer's Disease.