使用电子鼻传感器阵列信号诊断呼吸相关性肺炎：改进机器学习在呼吸研究中的应用的解决方案
diagnosis of ventilator-associated pneumonia using electronic nose sensor
array signals: solutions to improve the application of machine learning in respiratory research
使用电子鼻传感器阵列信号诊断呼吸相关性肺炎：改进机器学习在呼吸研究中的应用的解决方案
chung-yu chen1,， wei-chi lin2 ， hsiao-yu yang
abstract
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.
keywords: electronic nose, breath test, machine learning, ventilator-associated pneumonia, volatile organic compounds
背景
相关性肺炎（vap）是重症监护病房的重要死亡原因。早期诊断vap对提供适当的治疗和降低死亡率具有重要意义。发展一种、高精度的诊断方法是非常重要的。电子传感器的发明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，建立一个算法的过程通常是不清楚的，并阻止医生在临床实践中应用人工智能技术。建立模型和评估准确性的清晰过程是有保证的。本研究的目的是发展一个呼气测试的vap与一个标准化的协议的机器学习技术。
方法
我们进行了病例对照研究。这项研究于2017年2月至2019年6月在中国台湾南部一家医院的重症监护室登记受试者。以vap患者为病例组，非肺炎患者为对照组。我们收集了呼出气，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和一组用于测试。我们应用八种机器学习算法来建立预测模型，提高模型性能并提供估计的诊断精度。
结果
共33例，26例为对照组。采用8种机器学习算法，测试集的平均准确度为0.81±0.04，灵敏度为0.79±0.08，特异度为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±0.08，两者吻合较好。
结论
利用传感器阵列和机器学习技术检测vap具有良好的准确性。人工智能有可能帮助医生作出临床诊断。明确的数据处理协议和提高通用性所需的建模过程。
关键词：电子鼻、呼吸测试、机器学习、相关性肺炎、挥发性有机化合物
如何管控工地扬尘，加强安装扬尘在线监测系统
超声波测厚仪的探头种类有哪些
现场检测，稳定性好二氧化氯测试仪
GJ-2密封式化验制样粉碎机无粉尘污染
如何维护美国SUN节流阀
使用电子鼻传感器阵列信号诊断呼吸相关性肺炎：改进机器学习在呼吸研究中的应用的解决方案
恒温恒湿试验箱 温湿度测试结果的计算与评定
衬胶闸阀的产品特点及选用标准
哪些因素会影响散装机伸缩布袋的使用寿命呢？
气动蝶阀的使用特点：
海绵压缩测试仪器—泰仕特
土壤水势温度测定仪的原理
小米加工设备破碎提纯工艺要求
常州市管道疏通清淤检测排查修复
LS-35LD,50LD,75LD,100LD,120LD,150LD反压蒸煮锅技术参数
调节阀饱和水蒸气热物理性质
压缩机常见故障分析—电机烧毁
OILGEAR油泵
地磅秤维护方法
PV092L1K1T1NMRC
array signals: solutions to improve the application of machine learning in respiratory research
使用电子鼻传感器阵列信号诊断呼吸相关性肺炎：改进机器学习在呼吸研究中的应用的解决方案
chung-yu chen1,， wei-chi lin2 ， hsiao-yu yang
abstract
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.
keywords: electronic nose, breath test, machine learning, ventilator-associated pneumonia, volatile organic compounds
背景
相关性肺炎（vap）是重症监护病房的重要死亡原因。早期诊断vap对提供适当的治疗和降低死亡率具有重要意义。发展一种、高精度的诊断方法是非常重要的。电子传感器的发明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，建立一个算法的过程通常是不清楚的，并阻止医生在临床实践中应用人工智能技术。建立模型和评估准确性的清晰过程是有保证的。本研究的目的是发展一个呼气测试的vap与一个标准化的协议的机器学习技术。
方法
我们进行了病例对照研究。这项研究于2017年2月至2019年6月在中国台湾南部一家医院的重症监护室登记受试者。以vap患者为病例组，非肺炎患者为对照组。我们收集了呼出气，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和一组用于测试。我们应用八种机器学习算法来建立预测模型，提高模型性能并提供估计的诊断精度。
结果
共33例，26例为对照组。采用8种机器学习算法，测试集的平均准确度为0.81±0.04，灵敏度为0.79±0.08，特异度为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±0.08，两者吻合较好。
结论
利用传感器阵列和机器学习技术检测vap具有良好的准确性。人工智能有可能帮助医生作出临床诊断。明确的数据处理协议和提高通用性所需的建模过程。
关键词：电子鼻、呼吸测试、机器学习、相关性肺炎、挥发性有机化合物
如何管控工地扬尘，加强安装扬尘在线监测系统
超声波测厚仪的探头种类有哪些
现场检测，稳定性好二氧化氯测试仪
GJ-2密封式化验制样粉碎机无粉尘污染
如何维护美国SUN节流阀
使用电子鼻传感器阵列信号诊断呼吸相关性肺炎：改进机器学习在呼吸研究中的应用的解决方案
恒温恒湿试验箱 温湿度测试结果的计算与评定
衬胶闸阀的产品特点及选用标准
哪些因素会影响散装机伸缩布袋的使用寿命呢？
气动蝶阀的使用特点：
海绵压缩测试仪器—泰仕特
土壤水势温度测定仪的原理
小米加工设备破碎提纯工艺要求
常州市管道疏通清淤检测排查修复
LS-35LD,50LD,75LD,100LD,120LD,150LD反压蒸煮锅技术参数
调节阀饱和水蒸气热物理性质
压缩机常见故障分析—电机烧毁
OILGEAR油泵
地磅秤维护方法
PV092L1K1T1NMRC