Multi-task longitudinal forecasting with missing values on Alzheimer's disease

Carlos Sevilla-Salcedo, Vandad Imani, Pablo M. Olmos, Vanessa Gómez-Verdejo, Jussi Tohka

Abstract: Highlights•Novel Bayesian variational inference framework for multisource longitudinal data with multitask classification/regression in dementia.•Semi-supervised formulation to handle high number of missing values and exploit temporal data structure.•Latent representations to combine time-stamps and obtain interpretable results.•Performance improvement over baselines in the simultaneous prediction of diagnosis, ventricle volume and ADAS13 score.