Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression

In this paper, we propose a novel deep recurrent neural network as an Alzheimer’s Disease (AD) progression model, capable of jointly conducting tasks of missing values imputation, phenotypic measurements forecast, and clinical state prediction of a subject based on his/her longitudinal imaging biomarkers. Unlike the existing methods that mostly ignore missing values or impute them by means of an independent statistical model before training a disease progression model, we devise a unified recurrent network architecture for jointly performing missing values imputation, biomarker values forecast, and clinical state prediction from the longitudinal data. For these tasks to be handled in a unified framework, we also define an objective function that can be efficiently optimized by means of stochastic gradient descent in an end-to-end manner. We validated the effectiveness of our proposed method by comparing with the comparative methods over the TADPOLE challenge cohort.