Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer's Disease

被引:15
作者
Brand, Lodewijk [1 ]
Wang, Hua [1 ]
Huang, Heng [2 ]
Risacher, Shannon [3 ]
Saykin, Andrew [3 ]
Shen, Li [3 ,4 ]
机构
[1] Colorado Sch Mines, Dept Comp Sci, Golden, CO 80401 USA
[2] Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA USA
[3] Indiana Univ, Dept Radiol & Imaging Sci, Dept BioHlth Informat, Indianapolis, IN 46204 USA
[4] Univ Penn, Dept Biostat Epidemiol & Informat, Philadelphia, PA 19104 USA
来源
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT I | 2018年 / 11070卷
关键词
Alzheimer's disease; Multi-modal; Longitudinal; Tensor; BIOMARKERS; MRI; ASSOCIATION; GENOTYPE; ATROPHY; AD;
D O I
10.1007/978-3-030-00928-1_63
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Alzheimer's disease (AD) is a degenerative brain disease that affects millions of people around the world. As populations in the United States and worldwide age, the prevalence of Alzheimer's disease will only increase. In turn, the social and financial costs of AD will create a difficult environment for many families and caregivers across the globe. By combining genetic information, brain scans, and clinical data, gathered over time through the Alzheimer's Disease Neuroimaging Initiative (ADNI), we propose a new Joint High-Order Multi-Modal Multi-Task Feature Learning method to predict the cognitive performance and diagnosis of patients with and without AD.
引用
收藏
页码:555 / 562
页数:8
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