Multi-task longitudinal forecasting with missing values on Alzheimer's Disease
This work addresses forecasting challenges in dementia research by handling missing values and multi-task learning, though it is incremental as it applies an existing model to a specific domain.
The paper tackled the problem of forecasting Alzheimer's Disease by jointly learning multiple tasks on longitudinal data with missing values, using the SSHIBA model to impute missing data and predict diagnosis, ventricle volume, and clinical scores, resulting in outperformance of baselines.
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.