Predicting Alzheimer's Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data.

Lin, Weiming; Gao, Qinquan; Yuan, Jiangnan; Chen, Zhiying; Feng, Chenwei; Chen, Weisheng; Du, Min; Tong, Tong

Lin, Weiming; Gao, Qinquan; Yuan, Jiangnan; Chen, Zhiying; Feng, Chenwei; Chen, Weisheng; Du, Min; Tong, Tong.

Afiliação

Lin W; School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
Gao Q; College of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Yuan J; College of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Chen Z; Imperial Vision Technology, Fuzhou, China.
Feng C; School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
Chen W; Fujian Key Laboratory of Communication Network and Information Processing, Xiamen University of Technology, Xiamen, China.
Du M; School of Electrical Engineering & Automation, Xiamen University of Technology, Xiamen, China.
Tong T; School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.

Front Aging Neurosci ; 12: 77, 2020.

Article em En | MEDLINE | ID: mdl-32296326

Palavras-chave

Alzheimer's disease; extreme learning machine; mild cognitive impairment; multimodal; prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Aging Neurosci Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China País de publicação: Suíça

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