Learning from class-imbalance and heterogeneous data for 30-day hospital readmission

Abstract

Abstract Predicting 30-day hospital readmission is a core research task in the development of personalized healthcare. However, the imbalanced class distribution and the heterogeneity of electronic health records are the major challenges to establish an effective machine learning model for this task. To overcome these issues, we propose a new 30-day readmission prediction algorithm to improve the performance. First, we solve the problem of class-imbalance readmission prediction by learning sample weights based on hypothesis margin loss. At the same time, we consider the character of data heterogeneity, and learn the weights of heterogeneous data sources to improve the generalization ability. Based on this, we construct an optimization framework, which involves two variables, i.e., sample weights and source weights. By iterative optimization, we obtain the prediction result for readmission. Finally, we conduct experiments on three real-world readmission datasets to verify the effectiveness of the proposed method. The experimental results show that the proposed algorithm has the advantages to deal with the task of 30-day hospital readmission prediction.