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Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning
Patients whose lungs are compromised due to various respiratory health concerns require mechanical ventilation for support in breathing. Different mechanical ventilation settings are selected depending on the patient’s lung condition, and the selection of these parameters depends on the observed pat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150272/ https://www.ncbi.nlm.nih.gov/pubmed/34067153 http://dx.doi.org/10.3390/bioengineering8050060 |
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author | Oruganti Venkata, Sanjay Sarma Koenig, Amie Pidaparti, Ramana M. |
author_facet | Oruganti Venkata, Sanjay Sarma Koenig, Amie Pidaparti, Ramana M. |
author_sort | Oruganti Venkata, Sanjay Sarma |
collection | PubMed |
description | Patients whose lungs are compromised due to various respiratory health concerns require mechanical ventilation for support in breathing. Different mechanical ventilation settings are selected depending on the patient’s lung condition, and the selection of these parameters depends on the observed patient response and experience of the clinicians involved. To support this decision-making process for clinicians, good prediction models are always beneficial in improving the setting accuracy, reducing treatment error, and quickly weaning patients off the ventilation support. In this study, we developed a machine learning model for estimation of the mechanical ventilation parameters for lung health. The model is based on inverse mapping of artificial neural networks with the Graded Particle Swarm Optimizer. In this new variant, we introduced grouping and hierarchy in the swarm in addition to the general rules of particle swarm optimization to further improve its prediction performance of the mechanical ventilation parameters. The machine learning model was trained and tested using clinical data from canine and feline patients at the University of Georgia College of Veterinary Medicine. Our model successfully generated a range of parameter values for the mechanical ventilation applied on test data, with the average prediction values over multiple trials close to the target values. Overall, the developed machine learning model should be able to predict the mechanical ventilation settings for various respiratory conditions for patient’s survival once the relevant data are available. |
format | Online Article Text |
id | pubmed-8150272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81502722021-05-27 Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning Oruganti Venkata, Sanjay Sarma Koenig, Amie Pidaparti, Ramana M. Bioengineering (Basel) Article Patients whose lungs are compromised due to various respiratory health concerns require mechanical ventilation for support in breathing. Different mechanical ventilation settings are selected depending on the patient’s lung condition, and the selection of these parameters depends on the observed patient response and experience of the clinicians involved. To support this decision-making process for clinicians, good prediction models are always beneficial in improving the setting accuracy, reducing treatment error, and quickly weaning patients off the ventilation support. In this study, we developed a machine learning model for estimation of the mechanical ventilation parameters for lung health. The model is based on inverse mapping of artificial neural networks with the Graded Particle Swarm Optimizer. In this new variant, we introduced grouping and hierarchy in the swarm in addition to the general rules of particle swarm optimization to further improve its prediction performance of the mechanical ventilation parameters. The machine learning model was trained and tested using clinical data from canine and feline patients at the University of Georgia College of Veterinary Medicine. Our model successfully generated a range of parameter values for the mechanical ventilation applied on test data, with the average prediction values over multiple trials close to the target values. Overall, the developed machine learning model should be able to predict the mechanical ventilation settings for various respiratory conditions for patient’s survival once the relevant data are available. MDPI 2021-05-07 /pmc/articles/PMC8150272/ /pubmed/34067153 http://dx.doi.org/10.3390/bioengineering8050060 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Oruganti Venkata, Sanjay Sarma Koenig, Amie Pidaparti, Ramana M. Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning |
title | Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning |
title_full | Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning |
title_fullStr | Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning |
title_full_unstemmed | Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning |
title_short | Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning |
title_sort | mechanical ventilator parameter estimation for lung health through machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150272/ https://www.ncbi.nlm.nih.gov/pubmed/34067153 http://dx.doi.org/10.3390/bioengineering8050060 |
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