Department of Mechanical Engineering, Yuan Ze University, Chungli 32003, Taiwan.
College of Mechanical Engineering, Guizhou University, Guiyang 550025, China.
Sensors (Basel). 2019 Apr 18;19(8):1866. doi: 10.3390/s19081866.
One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In this study, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients' data to predict whether they are infected with VAP with infection. Furthermore, in order to improve the accuracy and the generalization of the prediction models, the ensemble neural networks (ENN) method was applied. In this study, ENN and SVM prediction models were trained and tested. In order to evaluate the models' performance, a fivefold cross-validation method was applied. The results showed that both ENN and SVM models have high recognition rates of VAP with infection, with 0.9479 ± 0.0135 and 0.8686 ± 0.0422 accuracies, 0.9714 ± 0.0131, 0.9250 ± 0.0423 sensitivities, and 0.9288 ± 0.0306, 0.8639 ± 0.0276 positive predictive values, respectively. The ENN model showed better performance compared to SVM in the recognition of VAP with infection. The areas under the receiver operating characteristic curve of the two models were 0.9842 ± 0.0058 and 0.9410 ± 0.0301, respectively, showing that both models are very stable and accurate classifiers. This study aims to assist the physician in providing a scientific and effective reference for performing early detection in infection or other diseases.
患者的一个关注点是在重症监护病房使用呼吸机后离线检测肺炎感染状态。因此，提出了用于呼吸机相关性肺炎（VAP）快速诊断的机器学习方法。一种流行的设备，Cyranose 320 e-nose，通常用于肺病研究，它是一个高度集成的系统和传感器，包含 32 个使用聚合物和碳黑材料的阵列。在这项研究中，共涉及 24 名受试者，包括 12 名感染肺炎的受试者和其余未感染的受试者。使用三层反向传播人工神经网络和支持向量机（SVM）方法对患者数据进行分析，以预测他们是否感染了 VAP。此外，为了提高预测模型的准确性和泛化能力，应用了集成神经网络（ENN）方法。在这项研究中，训练和测试了 ENN 和 SVM 预测模型。为了评估模型的性能，应用了五重交叉验证方法。结果表明，ENN 和 SVM 模型对 VAP 感染的识别率都很高，分别为 0.9479 ± 0.0135 和 0.8686 ± 0.0422 的准确率，0.9714 ± 0.0131，0.9250 ± 0.0423 的灵敏度，0.9288 ± 0.0306，0.8639 ± 0.0276 的阳性预测值。ENN 模型在识别 VAP 感染方面的性能优于 SVM。两个模型的接收者操作特征曲线下面积分别为 0.9842 ± 0.0058 和 0.9410 ± 0.0301，表明两个模型都是非常稳定和准确的分类器。本研究旨在为医生提供科学有效的参考，以便进行早期检测感染或其他疾病。
