Intensive care unit (ICU) readmissions of critically ill patients result in significant increases in mortality rates and costs, but most readmissions could be avoided. Therefore, the medical management community has devoted considerable effort to developing predictive classifications for ICU readmissions. However, the existing classification methods lack effective feature engineering and are dependent on large quantity of imbalanced and sparse data. In this paper, we use an objective quantitative data set to estimate the probability of ICU readmission for patients who have been transferred from the ICU to the general ward at various risk levels. To implement valuable feature selection for imbalanced time series data, we integrate the missing value analysis and the likelihood ratio test for the distribution characteristics of time series indicators and introduce a weight decay random forest model to achieve ICU readmission classification based on sparse data. Using these approaches, we can rank the most relevant factors that affect the probability of ICU readmission and identify the missing indicators that have the greatest impact on ICU readmission classification. Comprehensive experimental results show that our proposed method can outperform other traditional methods according to seven different performance indicators.

重症患者的重症监护病房 (ICU) 再入院会导致死亡率和成本显着增加，但大多数再入院是可以避免的。因此，医学管理界已投入大量精力来开发 ICU 再入院的预测分类。然而，现有的分类方法缺乏有效的特征工程，并且依赖于大量不平衡和稀疏的数据。在本文中，我们使用客观的定量数据集来估计从 ICU 转入普通病房的患者在不同风险水平下再次入住 ICU 的概率。为不平衡的时间序列数据实现有价值的特征选择，针对时间序列指标的分布特征，我们结合缺失值分析和似然比检验，引入权重衰减随机森林模型，实现基于稀疏数据的ICU再入院分类。使用这些方法，我们可以对影响 ICU 再入院概率的最相关因素进行排序，并确定对 ICU 再入院分类影响最大的缺失指标。综合实验结果表明，根据七个不同的性能指标，我们提出的方法可以优于其他传统方法。我们可以对影响ICU再入院概率的最相关因素进行排序，并找出对ICU再入院分类影响最大的缺失指标。综合实验结果表明，根据七个不同的性能指标，我们提出的方法可以优于其他传统方法。我们可以对影响ICU再入院概率的最相关因素进行排序，并找出对ICU再入院分类影响最大的缺失指标。综合实验结果表明，根据七个不同的性能指标，我们提出的方法可以优于其他传统方法。