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Lei B, Jiang F, Chen S, et al. Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning. Front Aging Neurosci. 2017;9:6doi: 10.3389/fnagi.2017.00006.
Lei, B., Jiang, F., Chen, S., Ni, D., & Wang, T. (2017). Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning. Frontiers in aging neuroscience, 96. https://doi.org/10.3389/fnagi.2017.00006
Lei, Baiying, et al. "Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning." Frontiers in aging neuroscience vol. 9 (2017): 6. doi: https://doi.org/10.3389/fnagi.2017.00006
Lei B, Jiang F, Chen S, Ni D, Wang T. Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning. Front Aging Neurosci. 2017 Mar 03;9:6. doi: 10.3389/fnagi.2017.00006. eCollection 2017. PMID: 28316569; PMCID: PMC5335657.
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Front Aging Neurosci. 2017 Mar 03;9:6. doi: 10.3389/fnagi.2017.00006. eCollection 2017.

Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning.

Frontiers in aging neuroscience

Baiying Lei, Feng Jiang, Siping Chen, Dong Ni, Tianfu Wang

Affiliations

  1. School of Biomedical Engineering, Shenzhen UniversityShenzhen, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen UniversityShenzhen, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen UniversityShenzhen, China; Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang UniversityFuzhou, China.
  2. School of Biomedical Engineering, Shenzhen UniversityShenzhen, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen UniversityShenzhen, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen UniversityShenzhen, China.

PMID: 28316569 PMCID: PMC5335657 DOI: 10.3389/fnagi.2017.00006

Abstract

It is highly desirable to predict the progression of Alzheimer's disease (AD) of patients [e.g., to predict conversion of mild cognitive impairment (MCI) to AD], especially longitudinal prediction of AD is important for its early diagnosis. Currently, most existing methods predict different clinical scores using different models, or separately predict multiple scores at different future time points. Such approaches prevent coordinated learning of multiple predictions that can be used to jointly predict clinical scores at multiple future time points. In this paper, we propose a joint learning method for predicting clinical scores of patients using multiple longitudinal prediction models for various future time points. Three important relationships among training samples, features, and clinical scores are explored. The relationship among different longitudinal prediction models is captured using a common feature set among the multiple prediction models at different time points. Our experimental results based on the Alzheimer's disease neuroimaging initiative (ADNI) database shows that our method achieves considerable improvement over competing methods in predicting multiple clinical scores.

Keywords: Alzheimer's disease (AD); feature selection; joint learning; longitudinal analysis; prediction

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