A Technique for Monitoring Mechanically Ventilated Patient Lung Conditions
Background: Mechanical ventilation is a critical but resource-intensive treatment. Automated tools are common in screening diagnostics, whereas real-time, continuous trend analysis in mechanical ventilation remains rare. Current techniques for monitoring lung conditions are often invasive, lack accu...
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MDPI AG
2024-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2616 |
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| author | Pieter Marx Henri Marais |
| author_facet | Pieter Marx Henri Marais |
| author_sort | Pieter Marx |
| collection | DOAJ |
| description | Background: Mechanical ventilation is a critical but resource-intensive treatment. Automated tools are common in screening diagnostics, whereas real-time, continuous trend analysis in mechanical ventilation remains rare. Current techniques for monitoring lung conditions are often invasive, lack accuracy, and fail to isolate respiratory resistance—making them impractical for continuous monitoring and diagnosis. To address this challenge, we propose an automated, non-invasive condition monitoring method to support pulmonologists. Methods: Our method leverages ventilation waveform time-series data in controlled modes to monitor lung conditions automatically and non-invasively on a breath-by-breath basis while accurately isolating respiratory resistance. Results: Using statistical classification and regression models, the approach achieves 99.1% accuracy for ventilation mode classification, 97.5% accuracy for feature extraction, and 99.0% for predicting mechanical lung parameters. The models are both computationally efficient (720 K predictions per second per core) and lightweight (24.5 MB). Conclusions: By storing breath-by-breath predictions, pulmonologists can access a high-resolution trend of lung conditions, gaining clear insights into sudden changes without speculation and streamlining diagnosis and decision-making. The deployment of this solution could expand domain knowledge, enhance the understanding of patient conditions, and enable real-time dashboards for parallel monitoring, helping to prioritize patients and optimize resource use, which is especially valuable during pandemics. |
| format | Article |
| id | doaj-art-258d2c1bdbd24cdd815b6afd818d026c |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-258d2c1bdbd24cdd815b6afd818d026c2024-12-13T16:24:24ZengMDPI AGDiagnostics2075-44182024-11-011423261610.3390/diagnostics14232616A Technique for Monitoring Mechanically Ventilated Patient Lung ConditionsPieter Marx0Henri Marais1Faculty of Engineering, North-West University, Potchefstroom 2531, South AfricaFaculty of Engineering, North-West University, Potchefstroom 2531, South AfricaBackground: Mechanical ventilation is a critical but resource-intensive treatment. Automated tools are common in screening diagnostics, whereas real-time, continuous trend analysis in mechanical ventilation remains rare. Current techniques for monitoring lung conditions are often invasive, lack accuracy, and fail to isolate respiratory resistance—making them impractical for continuous monitoring and diagnosis. To address this challenge, we propose an automated, non-invasive condition monitoring method to support pulmonologists. Methods: Our method leverages ventilation waveform time-series data in controlled modes to monitor lung conditions automatically and non-invasively on a breath-by-breath basis while accurately isolating respiratory resistance. Results: Using statistical classification and regression models, the approach achieves 99.1% accuracy for ventilation mode classification, 97.5% accuracy for feature extraction, and 99.0% for predicting mechanical lung parameters. The models are both computationally efficient (720 K predictions per second per core) and lightweight (24.5 MB). Conclusions: By storing breath-by-breath predictions, pulmonologists can access a high-resolution trend of lung conditions, gaining clear insights into sudden changes without speculation and streamlining diagnosis and decision-making. The deployment of this solution could expand domain knowledge, enhance the understanding of patient conditions, and enable real-time dashboards for parallel monitoring, helping to prioritize patients and optimize resource use, which is especially valuable during pandemics.https://www.mdpi.com/2075-4418/14/23/2616classification modelscondition monitoringlungmachine learningmechanical ventilationpulmonary diseases |
| spellingShingle | Pieter Marx Henri Marais A Technique for Monitoring Mechanically Ventilated Patient Lung Conditions Diagnostics classification models condition monitoring lung machine learning mechanical ventilation pulmonary diseases |
| title | A Technique for Monitoring Mechanically Ventilated Patient Lung Conditions |
| title_full | A Technique for Monitoring Mechanically Ventilated Patient Lung Conditions |
| title_fullStr | A Technique for Monitoring Mechanically Ventilated Patient Lung Conditions |
| title_full_unstemmed | A Technique for Monitoring Mechanically Ventilated Patient Lung Conditions |
| title_short | A Technique for Monitoring Mechanically Ventilated Patient Lung Conditions |
| title_sort | technique for monitoring mechanically ventilated patient lung conditions |
| topic | classification models condition monitoring lung machine learning mechanical ventilation pulmonary diseases |
| url | https://www.mdpi.com/2075-4418/14/23/2616 |
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