title: Rectified-Linear-Unit-Based Deep Learning for Biomedical Multi-label Data.
abstract: Disease diagnosis is one of the major data mining questions by the clinicians. The current diagnosis models usually have a strong assumption that one patient has only one disease, i.e. a single-label data mining problem. But the patients, especially when at the late stages, may have more than one disease and require a multi-label diagnosis. The multi-label data mining is much more difficult than a single-label one, and very few algorithms have been developed for this situation. Deep learning is a data mining algorithm with highly dense inner structure and has achieved many successful applications in the other areas. We propose a hypothesis that rectified-linear-unit-based deep learning algorithm may also be good at the clinical questions, by revising the last layer as a multi-label output. The proof-of-concept experimental data support the hypothesis, and the community may be interested in trying more applications.
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, People's Republic of China.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, People's Republic of China.|Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, Jiangxi, 333403, People's Republic of China.
authors.forename: Pu
authors.initials: P
authors.lastname: Wang
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, People's Republic of China.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, People's Republic of China.
authors.forename: Ruiquan
authors.initials: R
authors.lastname: Ge
authors.affiliation: Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, Jiangxi, 333403, People's Republic of China.
authors.forename: Xuan
authors.initials: X
authors.lastname: Xiao
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, People's Republic of China. yp.cai@siat.ac.cn.
authors.forename: Yunpeng
authors.initials: Y
authors.lastname: Cai
authors.affiliation: Department of Pathogenobiology, Basic Medical College of Jilin University, Changchun, Jilin, 130012, People's Republic of China. qing@jlu.edu.cn.
authors.forename: Guoqing
authors.initials: G
authors.lastname: Wang
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, People's Republic of China. FengfengZhou@gmail.com.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, People's Republic of China. FengfengZhou@gmail.com.
authors.forename: Fengfeng
authors.identifier: http://orcid.org/0000-0002-8108-6007
authors.initials: F
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D020811:Biomedical Technology; D000069550:Machine Learning; D012441:Saccharomyces cerevisiae / Q000378:metabolism
keywords: Clinical diagnosis; Deep learning; Multi-label classification; Rectified linear unit; Single-label classification