Deep learning for comprehensive forecasting of Alzheimer's Disease progression
This enables personalized forecasting of Alzheimer's Disease progression, which is crucial for personalized medicine, though it builds on existing deep learning methods.
The researchers tackled the problem of predicting only single endpoints in Alzheimer's Disease progression by developing an unsupervised deep learning model that simulates detailed 18-month patient trajectories for 44 clinical variables, achieving accuracy in predicting ADAS-Cog scores comparable to supervised models.
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.