4D Tensor Multi-task Continual Learning for Disease Dynamic Prediction

Abstract

Machine learning techniques for predicting Alzheimer's disease (AD) progression can substantially help researchers and clinicians establish strong AD preventive and treatment strategies. However, current research on AD prediction algorithms encounters challenges with monotonic data form, small dataset and scarcity of time-continuous data. To address all three of these problems at once, we propose a novel machine learning approach that implements the 4D tensor multi-task continual learning algorithm to predict AD progression by quantifying multi-dimensional information on brain structural variation and knowledge sharing between patients. To meet real-world application scenarios, the method can integrate knowledge from all available data as patient data increases to continuously update and optimise prediction results. To evaluate the performance of the proposed approach, we conducted extensive experiments utilising data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results demonstrate that the proposed approach has superior accuracy and stability in predicting various cognitive scores of AD progression compared to single-task learning, benchmarks and state-of-the-art multi-task regression methods. The proposed approach identifies structural brain variations in patients and utilises it to accurately predict and diagnose AD progression from magnetic resonance imaging (MRI) data alone, and the performance of the model improves as the MRI data increases.