@article{111099000be244e1bca3929066dcdf4d,
title = "Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion",
abstract = "In this work, we are interested in predicting the diagnostic statuses of potentially neurodegenerated patients using feature values derived from multi-modality neuroimaging data and biological data, which might be incomplete. Collecting the feature values into a matrix, with each row containing a feature vector of a sample, we propose a framework to predict the corresponding associated multiple target outputs (e.g., diagnosis label and clinical scores) from this feature matrix by performing matrix shrinkage following matrix completion. Specifically, we first combine the feature and target output matrices into a large matrix and then partition this large incomplete matrix into smaller submatrices, each consisting of samples with complete feature values (corresponding to a certain combination of modalities) and target outputs. Treating each target output as the outcome of a prediction task, we apply a 2-step multi-task learning algorithm to select the most discriminative features and samples in each submatrix. Features and samples that are not selected in any of the submatrices are discarded, resulting in a shrunk version of the original large matrix. The missing feature values and unknown target outputs of the shrunk matrix is then completed simultaneously. Experimental results using the ADNI dataset indicate that our proposed framework achieves higher classification accuracy at a greater speed when compared with conventional imputation-based classification methods and also yields competitive performance when compared with the state-of-the-art methods.",
keywords = "Classification, Data imputation, Matrix completion, Multi-task learning",
author = "Thung, {Kim Han} and Wee, {Chong Yaw} and Yap, {Pew Thian} and Dinggang Shen",
note = "Funding Information: This work was supported in part by NIH grants AG041721 , AG042599 , EB006733 , EB008374 , and EB009634 . This work was also partially supported by the National Research Foundation grant ( No. 2012-005741 ) funded by the Korean government. Funding Information: Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) National Institutes of Health grant U01 AG024904 . ADNI is funded by the National Institute on Aging , the National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following: Abbott, AstraZeneca AB, Amorfix, Bayer Schering Pharma AG, Bioclinica Inc., Biogen Idec, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, Innogenetics, IXICO, Janssen Alzheimer Immunotherapy, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Meso Scale Diagnostic, & LLC, Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Servier, Synarc, Inc., and Takeda Pharmaceuticals, as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 , K01 AG030514 , and the Dana Foundation . ",
year = "2014",
month = may,
day = "1",
doi = "10.1016/j.neuroimage.2014.01.033",
language = "English",
volume = "91",
pages = "386--400",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
}