  Research in Multiple Sclerosis (MS) has recently focused on extracting
knowledge from real-world clinical data sources. This type of data is more
abundant than data produced during clinical trials and potentially more
informative about real-world clinical practice. However, this comes at the cost
of less curated and controlled data sets. In this work, we address the task of
optimally extracting information from longitudinal patient data in the
real-world setting with a special focus on the sporadic sampling problem. Using
the MSBase registry, we show that with machine learning methods suited for
patient trajectories modeling, such as recurrent neural networks and tensor
factorization, we can predict disability progression of patients in a two-year
horizon with an ROC-AUC of 0.86, which represents a 33% decrease in the ranking
pair error (1-AUC) compared to reference methods using static clinical
features. Compared to the models available in the literature, this work uses
the most complete patient history for MS disease progression prediction.
