Empirical Analysis of Regularised Multi-Task Learning for Modelling Alzheimer's Disease Progression

Recently, there have been a wide spectrum of multitask learning (MTL) methods developed to model Alzheimer's disease (AD) progression. Typical MTL studies related cognitive ability prediction focus on modeling AD progression using high-quality clinical data such as MRI and cognitive scores. These studies follow a unified regularised MTL framework to process each follow-up data from patients over time. Beginning at baseline, the framework regards cognitive ability at each followup as a task and organise task relationship through temporal smoothness in cognitive ability. There is little attention on how to design feasible experimental protocols and normalisation for reliably evaluating those regularised MTL models. In this paper, we present an empirical analysis for investigate above issues. Four typical structural regularization approaches are revisited. Four issues affecting evaluation process of regularised MTL models are evaluated by experiments: 1) evaluation indicators, 2) repeated experimental times, 3) training data size and 4) number of tasks in MTL. The results demonstrate that regularised MTL models are capable of predicting AD progression with effectiveness, in many challenging cases of curse of dimensionality, data insufficiency or single MRI data input. One important finding is that MTL can effectively reduce the over-fitting risk of model, even with limited sample size. We also discover that the temporal smoothness assumption instead limits the performance of later tasks. It encourages us to revisit the relationship between patients' cognitive ability changes between 2 and 3 years when using MTL to model AD progression.