Multi-task Learning for Predicting Alzheimer's Disease Progression

Alzheimer's disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Identifying biomarkers that can track the progress of the disease has received much attentions in AD research. An accurate prediction of disease progression would facilitate optimal decision-making for clinicians and patients.

The aim of this project is to investigate multi-task learning approaches into predicting AD progression measured by the cognitive scores and selecting biomarkers predictive of the progression. This project will formulate the prediction problem as a multi-task regression problem by considering the approach of Temporal Group Lasso, which is a multi-task, regularised approach for the prediction of response variables that vary over time.

This objectives of this project include: 1) to investigate the concepts behind the Temporal Group LASSO and its related methods, as well as the type of potential applications in AD research. 2) to build up and implement a Temporal Group Lasso based AD progression model. 3) to evaluate the model using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 4) to analysis and discuss its future potential.