| Title: | Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research |
| Author(s): | Chen CY; Lin WC; Yang HY; |
| Address: | "Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital Yunlin Branch, Douliu, Taiwan. Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University College of Public Health, Taipei, Taiwan. Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University College of Public Health, Taipei, Taiwan. hyang@ntu.edu.tw. Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, Taipei, Taiwan. hyang@ntu.edu.tw. Department of Public Health, National Taiwan University College of Public Health, Taipei, Taiwan. hyang@ntu.edu.tw. Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei, Taiwan. hyang@ntu.edu.tw. Innovation and Policy Center for Population Health and Sustainable Environment, College of Public Health, National Taiwan University, Taipei, Taiwan. hyang@ntu.edu.tw" |
| DOI: | 10.1186/s12931-020-1285-6 |
| ISSN/ISBN: | 1465-993X (Electronic) 1465-9921 (Print) 1465-9921 (Linking) |
| 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: | "Aged Aged, 80 and over Biomedical Research/methods/*trends Case-Control Studies Decision Trees Electronic Nose/*trends Female Humans Intensive Care Units/trends Machine Learning/*trends Male Middle Aged Pneumonia, Ventilator-Associated/*diagnosis/*physiop;" |
| Notes: | "MedlineChen, Chung-Yu Lin, Wei-Chi Yang, Hsiao-Yu eng 106-2314-B-002-107, 107-2314-B-002-198, 108-2918-I-002-031, 107-3017-F-002-003/Ministry of Science and Technology, Taiwan/ NTU-107L9003/Ministry of Education/ England 2020/02/09 Respir Res. 2020 Feb 7; 21(1):45. doi: 10.1186/s12931-020-1285-6" |
