  The ability to predict the progression of biomarkers, notably in NDD, is
limited by the size of the longitudinal data sets, in terms of number of
patients, number of visits per patients and total follow-up time. To this end,
we introduce a data augmentation technique that is able to reproduce the
variability seen in a longitudinal training data set and simulate continuous
biomarkers trajectories for any number of virtual patients. Thanks to this
simulation framework, we propose to transform the training set into a simulated
data set with more patients, more time-points per patient and longer follow-up
duration. We illustrate this approach on the prediction of the MMSE of MCI
subjects of the ADNI data set. We show that it allows to reach predictions with
errors comparable to the noise in the data, estimated in test/retest studies,
achieving a improvement of 37% of the mean absolute error compared to the same
non-augmented model.
