Chiu Chih-Chou, Wu Chung-Min, Chien Te-Nien, Kao Ling-Jing, Li Chengcheng, Jiang Han-Ling
Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan.
College of Management, National Taipei University of Technology, Taipei 106, Taiwan.
J Clin Med. 2022 Oct 31;11(21):6460. doi: 10.3390/jcm11216460.
Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients' conditions by healthcare professionals but allow for a more optimal use of healthcare resources.
心血管疾病已被确认为全球三大死因之一，其发病和死亡主要归因于心力衰竭（HF）。在重症监护病房（ICU），心力衰竭患者的死亡风险增加，且消耗大量医疗资源，对高死亡风险患者的死亡时间进行早期准确预测，将使他们能够获得适当及时的医疗护理。本研究的数据来自MIMIC-III数据库，我们收集了6699名心力衰竭患者首次入住ICU的前24小时内的生命体征和检查结果。为了更精确地预测ICU中心力衰竭患者的死亡率，本文提出并应用了一种集成堆叠模型。在数据集分类的第一阶段，使用随机森林（RF）、支持向量机（SVC）、K近邻（KNN）、梯度提升机（LGBM）、装袋法（Bagging）和自适应增强算法（Adaboost）对数据集进行一级分类。然后，利用这六个分类器决策的融合结果来构建和优化二级分类器的堆叠集。结果表明，我们的模型在预测心力衰竭患者死亡率方面的准确率为95.25%，曲线下面积（AUROC）为82.55%，这证明了我们方法的卓越能力和效率。此外，本研究结果还表明，血小板、血糖和血尿素氮是对模型预测影响最大的临床特征。该分析结果不仅提高了医护人员对患者病情的了解，还能更优化地利用医疗资源。
