Multi-task Learning for Predicting Alzheimer's Disease Progression
Lead Research Organisation:
University of Sheffield
Department Name: Computer Science
Abstract
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.
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.
Organisations
People |
ORCID iD |
Po Yang (Primary Supervisor) | |
Xulong Wang (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/R513313/1 | 01/10/2018 | 30/09/2023 | |||
2784475 | Studentship | EP/R513313/1 | 07/02/2022 | 06/08/2025 | Xulong Wang |
EP/T517835/1 | 01/10/2020 | 30/09/2025 | |||
2784475 | Studentship | EP/T517835/1 | 07/02/2022 | 06/08/2025 | Xulong Wang |