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.
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Acknowledgements
This work was supported by the China 863 program (SS2015AA020109), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB13040400), the startup grant from the Jilin University, the grants from the National Natural Science Foundation of China (No. 31560316, No. 31260273, No. 61462047 and No. 61261027), and the Department of Education of JiangXi Province (GJJ13641). Constructive comments from the anonymous reviewers are greatly appreciated.
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Pu Wang and Ruiquan Ge contributed equally to this study.
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Wang, P., Ge, R., Xiao, X. et al. Rectified-Linear-Unit-Based Deep Learning for Biomedical Multi-label Data. Interdiscip Sci Comput Life Sci 9, 419–422 (2017). https://doi.org/10.1007/s12539-016-0196-1
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DOI: https://doi.org/10.1007/s12539-016-0196-1