  Machine learning techniques typically applied to dementia forecasting lack in
their capabilities to jointly learn several tasks, handle time dependent
heterogeneous data and missing values. In this paper, we propose a framework
using the recently presented SSHIBA model for jointly learning different tasks
on longitudinal data with missing values. The method uses Bayesian variational
inference to impute missing values and combine information of several views.
This way, we can combine different data-views from different time-points in a
common latent space and learn the relations between each time-point while
simultaneously modelling and predicting several output variables. We apply this
model to predict together diagnosis, ventricle volume, and clinical scores in
dementia. The results demonstrate that SSHIBA is capable of learning a good
imputation of the missing values and outperforming the baselines while
simultaneously predicting three different tasks.
