Compared to patients readmitted to general wards, readmitted patients in the intensive care unit (ICU) are exposed to higher mortality rates and prolonged hospital stays. Moreover, the readmission of ICU patients brings pressing challenges for ICU management. Most models are devoted to identifying the risk factors and developing classification models that can predict whether ICU patients will be readmitted. Though these models are prominent, they do not provide estimates for the frequency of readmissions. This paper establishes a prediction model, hybrid feature selection‐LightGBM (HFS‐LightGBM), to evaluate the probability and frequency of ICU patient readmissions empirically. In terms of feature selection, a hybrid feature selection (HFS) algorithm for LightGBM combines the filter and wrapper methods. Pearson's correlation coefficient is employed in the filter procedure. Then we adopt the targeted LightGBM classifier along with the recursive feature elimination and cross‐validated (RFECV) to produce the optimal feature subset. Additionally, the hyperparameters of the HFS‐LightGBM are optimized. The HFS‐LightGBM is employed on the real‐world ICU dataset containing 1722 patients' electronic health records. This model outperforms the current prevailing readmission models. The identified frequency can assist doctors in making specific interventions for patients to reduce the ICU readmission rate.

中文翻译：

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HFS‐LightGBM：一种基于混合特征选择的机器学习模型，用于对ICU患者再入院进行分类

与重诊普通病房的患者相比，重症监护病房（ICU）的重诊患者面临更高的死亡率和更长的住院时间。此外，重症监护病房患者的再住院给重症监护病房管理带来了紧迫的挑战。大多数模型致力于识别危险因素，并开发分类模型，以预测ICU患者是否会再次入院。尽管这些模型很突出，但它们没有提供重新录入频率的估计值。本文建立了一个预测模型，即混合特征选择-LightGBM（HFS-LightGBM），以经验方式评估ICU患者再次入院的可能性和频率。在特征选择方面，LightGBM的混合特征选择（HFS）算法结合了过滤器和包装器方法。皮尔逊 在滤波过程中采用了相关系数。然后，我们采用目标LightGBM分类器以及递归特征消除和交叉验证（RFECV）来生成最佳特征子集。此外，还优化了HFS-LightGBM的超参数。HFS-LightGBM用于包含1 722个患者电子健康记录的真实ICU数据集。该模型优于当前流行的再接纳模型。确定的频率可以帮助医生对患者进行特殊干预，以降低ICU再入院率。HFS-LightGBM用于包含1 722个患者电子健康记录的真实ICU数据集。该模型优于当前流行的再接纳模型。确定的频率可以帮助医生对患者进行特殊干预，以降低ICU再入院率。HFS-LightGBM用于包含1 722个患者电子健康记录的真实ICU数据集。该模型优于当前流行的再接纳模型。确定的频率可以帮助医生对患者进行特殊干预，以降低ICU再入院率。