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High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer Disease Progression Prediction
Hua Wang · Feiping Nie · Heng Huang · Jingwen Yan · Sungeun Kim · Shannon Risacher · Andrew Saykin · Li Shen

Tue Dec 04 04:40 PM -- 05:00 PM (PST) @ Harveys Convention Center Floor, CC

Alzheimer disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Regression analysis has been studied to relate neuroimaging measures to cognitive status. However, whether these measures have further predictive power to infer a trajectory of cognitive performance over time is still an under-explored but important topic in AD research. We propose a novel high-order multi-task learning model to address this issue. The proposed model explores the temporal correlations existing in data features and regression tasks by the structured sparsity-inducing norms. In addition, the sparsity of the model enables the selection of a small number of MRI measures while maintaining high prediction accuracy. The empirical studies, using the baseline MRI and serial cognitive data of the ADNI cohort, have yielded promising results.

Author Information

Hua Wang (Univ. of Texas at Arlington)
Feiping Nie (University of Texas Arlington)
Heng Huang (Electrical and Computer Engineering University of Pittsburgh)
Jingwen Yan (IUPUI)
Sungeun Kim (IUPUI)
Shannon Risacher (IUPUI)
Andrew Saykin (IUPUI)
Li Shen (Indiana University School of Medicine)

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