Su De, Zheng Jie, Shao Yue-Kai, Liu Jun-Ya, Liu Xin-Xin, Yu Kun, Feng Bang-Hai, Mei Hong, Qin Song
Department of Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, P.R. China.
Department of Critical Care Medicine, Zunyi Hospital of Traditional Chinese Medicine, Zunyi, Guizhou, P.R. China.
Digit Health. 2025 Apr 21;11:20552076251335705. doi: 10.1177/20552076251335705. eCollection 2025 Jan-Dec.
Although the assessment of in-hospital mortality risk among heart failure patients in the intensive care unit (ICU) is crucial for clinical decision-making, there is currently a lack of comprehensive models accurately predicting their prognosis. Machine learning techniques offer a powerful means to identify potential risk factors and predict outcomes within multivariable clinical data.
This study, based on the MIMIC-III database, extracted demographic characteristics, vital signs, laboratory test values, and comorbidity information of heart failure patients using structured query language. LASSO regression was employed for feature selection, and various machine learning algorithms were utilized to train models, including logistic regression (LR), random forest (RF), and gradient boosting (GB), among others. An ensemble learning model based on a soft voting mechanism was constructed. Model performance was evaluated using accuracy, recall, precision, F1 score, and AUC values through cross-validation and on an independent test set.
In five-fold cross-validation, the soft voting ensemble learning model demonstrated the best overall performance, with accuracy and AUC values both at 0.86. Additionally, RF and GB models also performed well, with RF achieving an accuracy of 0.79 and an AUC of 0.79 on the independent test set, while the GB model achieved an accuracy of 0.77 and an AUC of 0.79. In contrast, other models such as LR, SVM, and KNN exhibited poorer performance in terms of accuracy and AUC values, indicating the significant advantage of ensemble methods in handling complex clinical prediction tasks.
This study demonstrates the potential of machine learning models, particularly ensemble learning models based on soft voting mechanisms, in predicting in-hospital mortality risk among heart failure patients in the ICU. The overall performance of the ensemble learning model confirms its effectiveness as an adjunct clinical decision-making tool. Future research should further optimize the models and validate them in a broader patient population to enhance their practical utility and accuracy in real clinical settings.
尽管评估重症监护病房（ICU）中心力衰竭患者的院内死亡风险对于临床决策至关重要，但目前缺乏准确预测其预后的综合模型。机器学习技术为识别多变量临床数据中的潜在风险因素和预测结果提供了强大手段。
本研究基于MIMIC-III数据库，使用结构化查询语言提取心力衰竭患者的人口统计学特征、生命体征、实验室检查值和合并症信息。采用LASSO回归进行特征选择，并利用各种机器学习算法训练模型，包括逻辑回归（LR）、随机森林（RF）和梯度提升（GB）等。构建了基于软投票机制的集成学习模型。通过交叉验证和独立测试集，使用准确率、召回率、精确率、F1分数和AUC值评估模型性能。
在五折交叉验证中，软投票集成学习模型表现出最佳的整体性能，准确率和AUC值均为0.86。此外，RF和GB模型也表现良好，RF在独立测试集上的准确率为0.79，AUC为0.79，而GB模型的准确率为0.77，AUC为0.79。相比之下，LR、支持向量机（SVM）和K近邻（KNN）等其他模型在准确率和AUC值方面表现较差，表明集成方法在处理复杂临床预测任务方面具有显著优势。
本研究证明了机器学习模型，特别是基于软投票机制的集成学习模型，在预测ICU中心力衰竭患者院内死亡风险方面的潜力。集成学习模型的整体性能证实了其作为辅助临床决策工具的有效性。未来的研究应进一步优化模型，并在更广泛的患者群体中进行验证，以提高其在实际临床环境中的实用性和准确性。
