Deep learning architectures for multi-label classification of intelligent health risk prediction.

Journal: BMC bioinformatics
Published Date: Dec 28, 2017

Abstract

BACKGROUND: Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases.

Authors

  • Andrew Maxwell
    School of Computing, University of Southern Mississippi, Hattiesburg, MS, 39406, USA.
  • Runzhi Li
    Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China.
  • Bei Yang
    Cooperative Innovation Center of Internet Healthcare, School of Information & Engineering, Zhengzhou University, Zhengzhou, 450000, China.
  • Heng Weng
    Department of Big Medical Data, Health Construction Administration Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. ww128@qq.com.
  • Aihua Ou
    Department of Big Medical Data, Health Construction Administration Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Huixiao Hong
    National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA. Electronic address: Huixiao.Hong@fda.hhs.gov.
  • Zhaoxian Zhou
    a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA.
  • Ping Gong
    Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Chaoyang Zhang
    a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA.

Keywords

External Resources

Popular Topics
Recent Journals