Using deep learning for comprehensive, personalized forecasting of Alzheimer's Disease progression

A patient is more than one number, yet most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative -- using unsupervised deep learning to simulate detailed patient trajectories. We use data comprising 18-month longitudinal trajectories of 42 clinical variables from 1908 patients with Mild Cognitive Impairment (MCI) or Alzheimer's Disease (AD) to train a model for personalized forecasting of disease progression. Our model simulates 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. Even though it is not trained to predict changes in disease severity, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models. We show how simulations can be used to interpret our model and demonstrate how to create synthetic control arm data for AD clinical trials. Our model's ability to simultaneously predict dozens of characteristics of a patient at any point in the future is a crucial step forward in computational precision medicine.