Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease.

ABSTRACT: Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have also been used to estimate continuous clinical variables from brain images. However, most existing regression methods focus on estimating multiple clinical variables separately and thus cannot utilize the intrinsic useful correlation information among different clinical variables. On the other hand, in those regression methods, only a single modality of data (usually only the structural MRI) is often used, without considering the complementary information that can be provided by different modal

SUBMITTER: Zhang D 

PROVIDER: S-EPMC3230721 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature