Recurrent disease progression networks for modelling risk trajectory of heart failure.

Journal: PloS one
Published Date: Jan 6, 2021

Abstract

MOTIVATION: Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-dimensional data. However, very few studies have explored the ability of RNN in predicting long-term trajectories of recurrent events, which is more informative than predicting one single event in directing medical intervention.

Authors

  • Xing Han Lu
    School of Computer Science, McGill University, Montreal, QC, Canada.
  • Aihua Liu
    McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada.
  • Shih-Chieh Fuh
    School of Computer Science, McGill University, Montreal, Canada.
  • Yi Lian
    Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.
  • Liming Guo
    McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada.
  • Yi Yang
    Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Ariane Marelli
    McGill Adult Unit for Congenital Heart Disease Excellence (MAUDE Unit), Montreal, Canada.
  • Yue Li
    School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China.

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