Interpretable clinical prediction via attention-based neural network.

Journal: BMC medical informatics and decision making
Published Date: Jul 9, 2020

Abstract

BACKGROUND: The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in the last decade, which accumulated abundant electronic patient data, neural networks or deep learning techniques are gradually being applied to clinical tasks by utilizing the huge potential of EHR data. However, typical deep learning models are black-boxes, which are not transparent and the prediction outcomes of which are difficult to interpret.

Authors

  • Peipei Chen
    College of Biomedical Engineering and Instrumental Science, Zhejiang University, 310008 Hangzhou, China; School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
  • Wei Dong
    Department of Cardiology, Chinese PLA General Hospital, Beijing, China.
  • Jinliang Wang
    CardioCloud Medical Technology (Beijing) Co. Ltd, Beijing, 100084, China.
  • Xudong Lu
    The College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027 Hangzhou, Zhejiang, China.
  • Uzay Kaymak
    School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; College of Biomedical Engineering and Instrumental Science, Zhejiang University, 310008 Hangzhou, China.
  • Zhengxing Huang
    College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.

Keywords

External Resources

Popular Topics
Recent Journals