Before revision:  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 .
After revision:  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 and identifies sub-components associated with word recall as predictive of progression. The 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 .