Development of an E-nose system using machine learning methods to predict ventilator-associated pneumonia

Yu Hsuan Liao, Chung Hung Shih, Maysam F. Abbod, Jiann Shing Shieh, Yu Jen Hsiao

研究成果: 雜誌貢獻文章同行評審

7 引文 斯高帕斯(Scopus)

摘要

Currently, no device is available for the rapid screening of ventilator-associated pneumonia (VAP) at an early stage. Accordingly, we propose the design of an offline gas detection system to monitor and detect metabolites of pneumonia at an early stage. An electronic nose (e-nose) with 28 metal oxide semiconductor gas sensors was developed for predicting the presence of infection after patients have been intubated in the intensive care unit. The effectiveness of VAP identification was verified using clinical data. A total of 40 patients were included in this study, of whom 20 were infected with Pseudomonas aeruginosa and the remaining were uninfected. The results revealed that good accuracy rates of 0.9208% ± 0.0302% and 0.8547% ± 0.0214% were achieved by support vector machine and artificial neural network models, respectively. This study provides a simple, low-cost solution for the rapid screening of VAP at an early stage.

原文英語
期刊Microsystem Technologies
DOIs
出版狀態接受/付印 - 2020

ASJC Scopus subject areas

  • 電子、光磁材料
  • 凝聚態物理學
  • 硬體和架構
  • 電氣與電子工程

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