Towards graph-based class-imbalance learning for hospital readmission
作者：
Highlights：
• Provides a new optimization framework for solving the readmission prediction.
• Propose a graph-based method to deal with the class-imbalanced problem.
• Present an end-to-end trainable prediction model to improve the generalization.
• Applied the proposed method on six real-world readmission datasets.
• The method is proved to be effective in comparison to other methods.
摘要
•Provides a new optimization framework for solving the readmission prediction.•Propose a graph-based method to deal with the class-imbalanced problem.•Present an end-to-end trainable prediction model to improve the generalization.•Applied the proposed method on six real-world readmission datasets.•The method is proved to be effective in comparison to other methods.
论文关键词：Hospital readmission,Graph embedding,Class-imbalance learning,Neural network model
论文评审过程：Received 29 October 2020, Revised 8 January 2021, Accepted 23 February 2021, Available online 8 March 2021, Version of Record 1 April 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114791
