Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research
Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research
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DOI：
10.1186/s12931-020-1285-6
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发表时间：
2020-02-07
影响因子：
5.8
通讯作者：
Yang, Hsiao-Yu
中科院分区：
文献类型：
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作者：
Chen, Chung-Yu;Lin, Wei-Chi;Yang, Hsiao-Yu
背景：呼吸机相关性肺炎（VAP）是重症监护病房死亡的重要原因。VAP的早期诊断对于提供适当的治疗和降低死亡率至关重要。开发一种非侵入性和高度准确的诊断方法非常重要。电子传感器的发明已被应用于分析呼吸中的挥发性有机化合物，以使用机器学习技术检测VAP。然而，构建算法的过程通常是不明确的，这阻碍了医生在临床实践中应用人工智能技术。明确的模型构建过程和评估准确性是必要的。本研究的目的是为VAP开发一种具有机器学习技术标准化协议的呼吸测试。方法采用病例-对照研究。本研究于2017年2月至2019年6月在台湾南部一家医院的重症监护病房招募受试者。我们招募VAP患者为病例组，无肺炎的通气患者为对照组。我们收集了呼出气体，分析了电子鼻32个传感器阵列的电阻变化。我们将数据分成一组用于训练算法，另一组用于测试。我们应用了八种机器学习算法来构建预测模型，提高了模型性能并提供了估计的诊断准确性。结果本组共33例，对照组26例。采用8种机器学习算法，检测集的平均准确率为0.81 +/- 0.04，灵敏度为0.79 +/- 0.08，特异性为0.83 +/- 0.00，阳性预测值为0.85 +/- 0.02，阴性预测值为0.77 +/- 0.06，接收算子特征曲线下面积为0.85 +/- 0.04。测试集的平均kappa值为0.62 +/- 0.08，一致性较好。结论采用传感器阵列和机器学习技术检测VAP具有较好的准确性。人工智能有可能帮助医生进行临床诊断。明确的数据处理协议和建模过程，以提高通用性。
Background Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique. Methods We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy. Results A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 +/- 0.04, the sensitivity was 0.79 +/- 0.08, the specificity was 0.83 +/- 0.00, the positive predictive value was 0.85 +/- 0.02, the negative predictive value was 0.77 +/- 0.06, and the area under the receiver operator characteristic curves was 0.85 +/- 0.04. The mean kappa value in the testing set was 0.62 +/- 0.08, which suggested good agreement. Conclusions There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.