Multi-task Learning for Predicting Alzheimer's Disease Progression

预测阿尔茨海默病进展的多任务学习

• 批准号: 2784475
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2784475
• 关键词: Multi; task; Learning; Predicting; Alzheimer

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.

阿尔茨海默病（Alzheimer's disease，AD）是最常见的痴呆类型，是一种严重的神经退行性疾病。识别能够追踪疾病进展的生物标志物在AD研究中受到了广泛关注。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，通过认知评分预测AD进展，并选择预测进展的生物标志物。本研究计划将利用时群Lasso方法，将预测问题转化为多任务回归问题，该方法是一种多任务、正则化的方法，用于预测随时间变化的响应变量。本研究计划的目标包括：1）研究时群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)建立并实现基于时间组Lasso的AD进展模型。3)使用阿尔茨海默病神经成像倡议（ADNI）的数据评估模型。4)分析和讨论其未来的潜力。