Deep learning for healthcare: review, opportunities and challenges

Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of…

• NS
  National Science Foundation

  Awards: R01GM118609, 1650723
• NC
  National Cancer Institute

  Award: U54-CA189201-02
• NI
  National Institute of Diabetes and Digestive and Kidney Diseases

  Award: R01-DK098242-03
• UN
  U.S. National Library of Medicine
• DO
  Division of Information and Intelligent Systems
• NC
  National Center for Advancing Translational Sciences

  Award: UL1TR000067