Ventilator pressure prediction employing voting regressor with time series data of patient breaths

Raza, Ali and Rustam, Furqan and Siddiqui, Hafeez Ur Rehman and Soriano Flores, Emmanuel and Vidal Mazón, Juan Luis and de la Torre Díez, Isabel and Ripoll, María Asunción Vicente and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, emmanuel.soriano@uneatlantico.es, juanluis.vidal@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2025) Ventilator pressure prediction employing voting regressor with time series data of patient breaths. Health Informatics Journal, 31 (1). ISSN 1460-4582

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	COVID-19, deep learning, machine learning, mechanical ventilation, ventilator pressure prediction
Subjects:	Subjects > Biomedicine Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production
Depositing User:	Sr Bibliotecario
Date Deposited:	25 Feb 2025 15:47
Last Modified:	25 Feb 2025 15:47
URI:	http://repositorio.funiber.org/id/eprint/16824

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