diagnosis of ventilator-associated pneumonia using electronic nose sensor
array signals: solutions to improve the application of machine learning in respiratory research
使用電子鼻傳感器陣列信號診斷呼吸相關(guān)性肺炎：改進(jìn)機(jī)器學(xué)習(xí)在呼吸研究中的應(yīng)用的解決方案
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
相關(guān)性肺炎（vap）是重癥監(jiān)護(hù)病房的重要死亡原因。早期診斷vap對提供適當(dāng)?shù)闹委熀徒档退劳雎示哂兄匾饬x。發(fā)展一種、高精度的診斷方法是非常重要的。電子傳感器的發(fā)明被應(yīng)用于分析呼吸中的揮發(fā)性有機(jī)化合物，以使用機(jī)器學(xué)習(xí)技術(shù)檢測vap。然而，建立一個算法的過程通常是不清楚的，并阻止醫(yī)生在臨床實踐中應(yīng)用人工智能技術(shù)。建立模型和評估準(zhǔn)確性的清晰過程是有保證的。本研究的目的是發(fā)展一個呼氣測試的vap與一個標(biāo)準(zhǔn)化的協(xié)議的機(jī)器學(xué)習(xí)技術(shù)。
方法
我們進(jìn)行了病例對照研究。這項研究于2017年2月至2019年6月在中國臺灣南部一家醫(yī)院的重癥監(jiān)護(hù)室登記受試者。以vap患者為病例組，非肺炎患者為對照組。我們收集了呼出氣，分析了電子鼻32個傳感器陣列的電阻變化。我們將數(shù)據(jù)分成一組用于訓(xùn)練算法和一組用于測試。我們應(yīng)用八種機(jī)器學(xué)習(xí)算法來建立預(yù)測模型，提高模型性能并提供估計的診斷精度。
結(jié)果
共33例，26例為對照組。采用8種機(jī)器學(xué)習(xí)算法，測試集的平均準(zhǔn)確度為0.81±0.04，靈敏度為0.79±0.08，特異度為0.83±0.00，陽性預(yù)測值為0.85±0.02，陰性預(yù)測值為0.77±0.06，接收算子特征曲線下面積為0.85±0.04。測試集的平均kappa值為0.62±0.08，兩者吻合較好。
結(jié)論
利用傳感器陣列和機(jī)器學(xué)習(xí)技術(shù)檢測vap具有良好的準(zhǔn)確性。人工智能有可能幫助醫(yī)生作出臨床診斷。明確的數(shù)據(jù)處理協(xié)議和提高通用性所需的建模過程。
關(guān)鍵詞：電子鼻、呼吸測試、機(jī)器學(xué)習(xí)、相關(guān)性肺炎、揮發(fā)性有機(jī)化合物