eprintid: 16824 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/01/68/24 datestamp: 2025-02-25 23:30:14 lastmod: 2025-02-25 23:30:15 status_changed: 2025-02-25 23:30:14 type: article metadata_visibility: show creators_name: Raza, Ali creators_name: Rustam, Furqan creators_name: Siddiqui, Hafeez Ur Rehman creators_name: Soriano Flores, Emmanuel creators_name: Vidal Mazón, Juan Luis creators_name: de la Torre Díez, Isabel creators_name: Ripoll, María Asunción Vicente creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: emmanuel.soriano@uneatlantico.es creators_id: juanluis.vidal@uneatlantico.es creators_id: creators_id: creators_id: title: Ventilator pressure prediction employing voting regressor with time series data of patient breaths ispublished: pub subjects: uneat_bm subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: COVID-19, deep learning, machine learning, mechanical ventilation, ventilator pressure prediction 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. date: 2025-01 publication: Health Informatics Journal volume: 31 number: 1 id_number: doi:10.1177/14604582241295912 refereed: TRUE issn: 1460-4582 official_url: http://doi.org/10.1177/14604582241295912 access: open language: en citation: Artículo Materias > Biomedicina Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Artículos y libros 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 document_url: http://repositorio.unic.co.ao/id/eprint/16824/1/raza-et-al-2025-ventilator-pressure-prediction-employing-voting-regressor-with-time-series-data-of-patient-breaths.pdf