Ventilator pressure prediction employing voting regressor with time series data of patient breaths
Artículo
Materias > Biomedicina
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Abierto
Inglés
Objectives: Mechanical ventilator plays a vital role in saving millions of lives. Patients with COVID-19 symptoms need a ventilator to survive during the pandemic. Studies have reported that the mortality rates rise from 50% to 97% in those requiring mechanical ventilation during COVID-19. The pumping of air into the patient’s lungs using a ventilator requires a particular air pressure. High or low ventilator pressure can result in a patient’s life loss as high air pressure in the ventilator causes the patient lung damage while lower pressure provides insufficient oxygen. Consequently, precise prediction of ventilator pressure is a task of great significance in this regard. The primary aim of this study is to predict the airway pressure in the ventilator respiratory circuit during the breath. Methods: A novel hybrid ventilator pressure predictor (H-VPP) approach is proposed. The ventilator exploratory data analysis reveals that the high values of lung attributes R and C during initial time step values are the prominent causes of high ventilator pressure. Results: Experiments using the proposed approach indicate H-VPP achieves a 0.78 R2, mean absolute error of 0.028, and mean squared error of 0.003. These results are better than other machine learning and deep learning models employed in this study. Conclusion: Extensive experimentation indicates the superior performance of the proposed approach for ventilator pressure prediction with high accuracy. Furthermore, performance comparison with state-of-the-art studies corroborates the superior performance of the proposed approach.
metadata
Raza, Ali; Rustam, Furqan; Siddiqui, Hafeez Ur Rehman; Soriano Flores, Emmanuel; Vidal Mazón, Juan Luis; de la Torre Díez, Isabel; Ripoll, María Asunción Vicente y Ashraf, Imran
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, emmanuel.soriano@uneatlantico.es, juanluis.vidal@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR
(2025)
Ventilator pressure prediction employing voting regressor with time series data of patient breaths.
Health Informatics Journal, 31 (1).
ISSN 1460-4582
![[img]](http://repositorio.uneatlantico.es/style/images/fileicons/text.png) |
Texto
raza-et-al-2025-ventilator-pressure-prediction-employing-voting-regressor-with-time-series-data-of-patient-breaths.pdf Available under License Creative Commons Attribution Non-commercial. Descargar (1MB) |
Resumen
Objectives: Mechanical ventilator plays a vital role in saving millions of lives. Patients with COVID-19 symptoms need a ventilator to survive during the pandemic. Studies have reported that the mortality rates rise from 50% to 97% in those requiring mechanical ventilation during COVID-19. The pumping of air into the patient’s lungs using a ventilator requires a particular air pressure. High or low ventilator pressure can result in a patient’s life loss as high air pressure in the ventilator causes the patient lung damage while lower pressure provides insufficient oxygen. Consequently, precise prediction of ventilator pressure is a task of great significance in this regard. The primary aim of this study is to predict the airway pressure in the ventilator respiratory circuit during the breath. Methods: A novel hybrid ventilator pressure predictor (H-VPP) approach is proposed. The ventilator exploratory data analysis reveals that the high values of lung attributes R and C during initial time step values are the prominent causes of high ventilator pressure. Results: Experiments using the proposed approach indicate H-VPP achieves a 0.78 R2, mean absolute error of 0.028, and mean squared error of 0.003. These results are better than other machine learning and deep learning models employed in this study. Conclusion: Extensive experimentation indicates the superior performance of the proposed approach for ventilator pressure prediction with high accuracy. Furthermore, performance comparison with state-of-the-art studies corroborates the superior performance of the proposed approach.
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | COVID-19, deep learning, machine learning, mechanical ventilation, ventilator pressure prediction |
| Clasificación temática: | Materias > Biomedicina Materias > Ingeniería |
| Divisiones: | Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica |
| Depositado: | 25 Feb 2025 23:30 |
| Ultima Modificación: | 25 Feb 2025 23:30 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/16824 |
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