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Joint Learning of Multiple Longitudinal Prediction Models by Exploring Internal Relations

  • Baiying Lei
  • Siping Chen
  • Dong NiEmail author
  • Tianfu WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

Longitudinal prediction of the brain disorder such as Alzheimer’s disease (AD) is important for possible early detection and early intervention. Given the baseline imaging and clinical data, it will be interesting to predict the progress of disease for an individual subject, such as predicting the conversion of Mild Cognitive Impairment (MCI) to AD, in the future years. Most existing methods predicted different clinical scores using different models, or predicted multiple scores at different future time points separately. This often misses the chance of coordinated learning of multiple prediction models for jointly predicting multiple clinical scores at multiple future time points. In this paper, we propose a novel method for joint learning of multiple longitudinal prediction models for multiple clinical scores at multiple future time points. First, for each longitudinal prediction model, we explore three important relationships among training samples, features, and clinical scores, respectively, for enhancing its learning. Then, we further introduce additional relation among different longitudinal prediction models for allowing them to select a common set of features from the baseline imaging and clinical data, with l2,1 sparsity constraint, for their joint training. We evaluate the performance of our joint prediction models with the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, showing much better performance than the state-of-the-art methods in predicting multiple clinical scores at multiple future time points.

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© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  1. 1.Department of Biomedical Engineering, School of MedicineShenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound ImagingShenzhenChina

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