  Computational models that forecast the progression of Alzheimer's disease at
the patient level are extremely useful tools for identifying high risk cohorts
for early intervention and treatment planning. The state-of-the-art work in
this area proposes models that forecast by using latent representations
extracted from the longitudinal data across multiple modalities, including
volumetric information extracted from medical scans and demographic info. These
models incorporate the time horizon, which is the amount of time between the
last recorded visit and the future visit, by directly concatenating a
representation of it to the data latent representation. In this paper, we
present a model which generates a sequence of latent representations of the
patient status across the time horizon, providing more informative modeling of
the temporal relationships between the patient's history and future visits. Our
proposed model outperforms the baseline in terms of forecasting accuracy and F1
score with the added benefit of robustly handling missing visits.
