Towards graph-based class-imbalance learning for hospital readmission. (15th August 2021)

• Record Type: Journal Article
• Authors: Du, Guodong; Zhang, Jia; Ma, Fenglong; Zhao, Min; Lin, Yaojin; Li, Shaozi
• Is Part Of: Expert systems with applications. Volume 176(2021)
• Publication Date: 2021-08-15
• Subjects: Hospital readmission -- Graph embedding -- Class-imbalance learning -- Neural network model; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114791
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Abstract: Predicting hospital readmission with effective machine learning techniques has attracted a great attention in recent years. The fundamental challenge of this task stems from characteristics of the data extracted from electronic health records (EHR), which are imbalanced class distributions. This challenge further leads to the failure of most existing models that only provide a partial understanding for the learning problem and result in a biased and inaccurate prediction. To address this challenge, we propose a new graph-based class-imbalance learning method by fully making use of the data from different classes. First, we conduct graph construction for learning the pattern discrimination from between-class and within-class data samples. Then we design an optimization framework to incorporate the constructed graphs to obtain a class-imbalance aware graph embedding and further alleviate performance degeneration. Finally, we design a neural network model as the classifier to conduct imbalanced classification, i.e., hospital readmission prediction. Comprehensive experiments on six real-world readmission datasets show that the proposed method outperforms state-of-the-art approaches in readmission prediction task.