Multi-task Learning for Predicting Alzheimer's Disease Progression

阿尔茨海默病（AD）是一种严重的神经退行性疾病，是最常见的痴呆症类型。在阿尔茨海默病的研究中，寻找能够追踪疾病进展的生物标志物受到了广泛的关注。准确预测疾病进展将有助于临床医生和患者做出最佳决策。本项目的目的是研究多任务学习方法，通过认知评分和选择预测进展的生物标志物来预测AD的进展。该项目将通过考虑时态组Lasso方法将预测问题制定为多任务回归问题，这是一种用于预测随时间变化的响应变量的多任务、正则化方法。该项目的目标包括：1)研究时间组LASSO及其相关方法背后的概念，以及在AD研究中的潜在应用类型。2)建立并实现了基于时间群套索的AD进展模型。3)利用阿尔茨海默病神经影像学倡议（ADNI）的数据对模型进行评估。4)分析和讨论其未来潜力。

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.