Hospital readmission is among the most critical issues in the healthcare system due to its high prevalence and cost. The improvement effort necessitates reliable prediction models which can identify high-risk patients effectively and enable healthcare practitioners to take a strategic approach. Using predictive analytics based on electronic health record (EHR) for hospital readmission is faced with multiple challenges such as high dimensionality and event sparsity of medical codes and the class imbalance. To response to these challenges, an analytical framework is proposed by data-driven approaches using hospital inpatient administrative data from a nationwide healthcare dataset. A joint ensemble-learning model, which combines the modified weight boosting algorithm with stacking algorithm, is developed and validated. Our study first explores the effects of different feature engineering methods, which effectively handles the challenge of medical vector representation and medical vector sparsity. Secondly, ensemble learning with the proposed modified weight boosting algorithm is used to tackle the class imbalance problem and improve predictability. Finally, we provide various misclassification costs by setting different weights for each class during model training. Using the framework with the proposed modified weight boosting algorithm improves overall model performance by 22.7% and recall from 0.726 to the highest of 0.891 comparing to the benchmark models. Hospital practitioners can also utilize the prediction results of different cost weight to select the most suitable readmission intervention for patients according to the penalty policy of Centers for Medicare and Medicaid Services (CMS) and the cost trade-off of their hospitals.

中文翻译：

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预测医院的再入院：联合整体学习模型

由于其较高的患病率和成本，医院的再入院率是医疗保健系统中最关键的问题之一。改进工作需要可靠的预测模型，该模型可以有效地识别高风险患者，并使医疗保健从业人员能够采取战略性方法。将基于电子健康记录（EHR）的预测分析用于医院再入院面临多种挑战，例如医疗法规的高维度和事件稀疏性以及班级失衡。为了应对这些挑战，通过数据驱动的方法提出了一个分析框架，该方法使用了来自全国医疗保健数据集的医院住院患者管理数据。开发并验证了将改进的权重提升算法与叠加算法相结合的联合集成学习模型。我们的研究首先探索了不同特征工程方法的效果，这些方法有效地应对了医学媒介表征和医学媒介稀疏性的挑战。其次，采用改进的权重提升算法进行集成学习，解决了班级不平衡问题，提高了可预测性。最后，我们通过在模型训练期间为每个班级设置不同的权重来提供各种误分类成本。与建议的改进权重算法一起使用该框架，与基准模型相比，将整体模型性能提高了22.7％，召回率从0.726提高到0.891的最高值。