Hospital readmission is one of the most important service quality measures. Recently, numerous risk assessment models have been proposed to address the hospital readmission problem. However, poor understanding of the class-imbalance hospital readmission data still challenges the development of accurate predictive models. To overcome the issue, a new risk prediction method termed joint imbalanced classification and feature selection (JICFS) is proposed for handling such a problem. To be specific, we construct the loss function within the large margin framework, in which the sample weight is involved to deal with the class imbalanced problem. Based on this, we design an optimization objective function involving ${\ell }_1$-norm regularization for improving the performance, and an iterative scheme is proposed to solve the optimization problem, thereby achieving feature selection to improve the performance. Finally, experimental results on six real-world hospital readmission datasets demonstrate that the proposed algorithm has the advantage compared with some state-of-the-art methods.

再次入院是最重要的服务质量衡量标准之一。最近，已经提出了许多风险评估模型来解决医院的再入院问题。但是，对班级失衡的医院再入院数据的了解不足，仍然对准确的预测模型的开发提出了挑战。为了解决这个问题，提出了一种称为联合不平衡分类和特征选择（JICFS）的新的风险预测方法来处理该问题。具体来说，我们在大余量框架内构造损失函数，其中涉及样本权重以处理类不平衡问题。基于此，我们设计了一个包含以下内容的优化目标函数：${\ell }_{\mathrm{1个}}$-norm规范化以提高性能，并提出了一种迭代方案来解决优化问题，从而实现特征选择以提高性能。最后，对六个真实世界医院再入院数据集的实验结果表明，与某些最新方法相比，该算法具有优势。