Deep learning architectures for multi-label classification of intelligent health risk prediction
作者: Andrew MaxwellRunzhi LiBei YangHeng WengAihua OuHuixiao HongZhaoxian ZhouPing GongChaoyang Zhang
作者单位: 1School of Computing, University of Southern Mississippi
2School of Information & Engineering, Zhengzhou University
3The Second Affiliated Hospital of Guangzhou University of Chinese Medicine
4Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration (FDA)
5Environmental Lab, US Army Engineer Research and Development Center
刊名: BMC Bioinformatics, 2017, Vol.18 (14)
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-017-1898-z
关键词: Deep neural networksDeep learningIntelligent health risk predictionMulti-label classificationMedical health records
英文摘要: 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.
原始语种摘要: 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.
全文获取路径: Springer Nature  (合作)
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影响因子:3.024 (2012)

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关键词翻译
关键词翻译
  • learning 学识
  • prediction 预报
  • mutually 互相
  • label 标签标记
  • exclusive 互斥
  • classification 分类
  • difficult 困难的
  • develop 发展
  • problem 题目
  • intelligent 有理性的