Towards graph-based class-imbalance learning for hospital readmission

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.