Randomized Multi-task Feature Learning Approach for Modelling and Predicting Alzheimer’s Disease Progression

Multi-task feature learning (MTFL) methods play a key role in predicting Alzheimer’s disease (AD) progression. These studies adhere to a unified feature-sharing framework to promote information exchange on relevant disease progression tasks. MTFL not only utilise the inherent properties of tasks to enhance prediction performance, but also yields weights that are capable to indicate nuanced changes of related AD biomarkers. Task regularized priors, however, introduced by MTFL lead to uncertainty in biomarkers selection, particularly amidst a plethora of highly interrelated biomarkers in a high dimensional space. There is little attention on studying how to design feasible experimental protocols for assessment of MTFL models. To narrow this knowledge gap, we proposed a Randomize Multi-task Feature Learning (RMFL) approach to effectively model and predict AD progression. As task increases, the results show that the RMFL is not only stable and interpretable, but also reduced by 0.2 in normalized mean square error compared to single-task models like Lasso, Ridge. Our method is also adaptable as a general regression framework to predict other chronic disease progression.