TY  - JOUR
A1  - Raza, Ali
A1  - Rustam, Furqan
A1  - Siddiqui, Hafeez Ur Rehman
A1  - Soriano Flores, Emmanuel
A1  - Vidal Mazón, Juan Luis
A1  - de la Torre Díez, Isabel
A1  - Ripoll, María Asunción Vicente
A1  - Ashraf, Imran
TI  - Ventilator pressure prediction employing voting regressor with time series data of patient breaths
VL  - 31
Y1  - 2025/01//
N2  - 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.
SN  - 1460-4582
AV  - public
JF  - Health Informatics Journal
ID  - uninimx16824
UR  - http://doi.org/10.1177/14604582241295912
KW  - COVID-19
KW  -  deep learning
KW  -  machine learning
KW  -  mechanical ventilation
KW  -  ventilator pressure prediction
IS  - 1
ER  -