Huang Kuo-Yang, Hsu Ying-Lin, Chen Huang-Chi, Horng Ming-Hwarng, Chung Che-Liang, Lin Ching-Hsiung, Xu Jia-Lang, Hou Ming-Hon
Division of Chest Medicine, Department of Internal Medicine, Changhua Christian Hospital, Changhua, Taiwan.
Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua, Taiwan.
Front Med (Lausanne). 2023 May 9;10:1167445. doi: 10.3389/fmed.2023.1167445. eCollection 2023.
Successful weaning from mechanical ventilation is important for patients admitted to intensive care units. However, models for predicting real-time weaning outcomes remain inadequate. Therefore, this study aimed to develop a machine-learning model for predicting successful extubation only using time-series ventilator-derived parameters with good accuracy.
Patients with mechanical ventilation admitted to the Yuanlin Christian Hospital in Taiwan between August 2015 and November 2020 were retrospectively included. A dataset with ventilator-derived parameters was obtained before extubation. Recursive feature elimination was applied to select the most important features. Machine-learning models of logistic regression, random forest (RF), and support vector machine were adopted to predict extubation outcomes. In addition, the synthetic minority oversampling technique (SMOTE) was employed to address the data imbalance problem. The area under the receiver operating characteristic (AUC), F1 score, and accuracy, along with the 10-fold cross-validation, were used to evaluate prediction performance.
In this study, 233 patients were included, of whom 28 (12.0%) failed extubation. The six ventilatory variables per 180 s dataset had optimal feature importance. RF exhibited better performance than the others, with an AUC value of 0.976 (95% confidence interval [CI], 0.975-0.976), accuracy of 94.0% (95% CI, 93.8-94.3%), and an F1 score of 95.8% (95% CI, 95.7-96.0%). The difference in performance between the RF and the original and SMOTE datasets was small.
The RF model demonstrated a good performance in predicting successful extubation in mechanically ventilated patients. This algorithm made a precise real-time extubation outcome prediction for patients at different time points.
对于入住重症监护病房的患者而言，成功撤机至关重要。然而，用于预测实时撤机结果的模型仍不完善。因此，本研究旨在开发一种仅使用呼吸机衍生的时间序列参数来准确预测成功拔管的机器学习模型。
回顾性纳入2015年8月至2020年11月期间在台湾员林基督教医院接受机械通气的患者。在拔管前获取包含呼吸机衍生参数的数据集。应用递归特征消除法来选择最重要的特征。采用逻辑回归、随机森林（RF）和支持向量机的机器学习模型来预测拔管结果。此外，采用合成少数过采样技术（SMOTE）来解决数据不平衡问题。使用受试者工作特征曲线下面积（AUC）、F1分数和准确率，以及10倍交叉验证来评估预测性能。
本研究共纳入233例患者，其中28例（12.0%）拔管失败。每180秒数据集中的六个通气变量具有最佳特征重要性。RF表现优于其他模型，AUC值为0.976（95%置信区间[CI]，0.975 - 0.976），准确率为94.0%（95%CI，93.8 - 94.3%），F1分数为95.8%（95%CI，95.7 - 96.0%）。RF与原始数据集和SMOTE数据集之间的性能差异较小。
RF模型在预测机械通气患者成功拔管方面表现良好。该算法能够在不同时间点对患者的拔管结果进行精确的实时预测。
