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Authors: Ghaida Alsaab and Sarah Alkhodair

Affiliation: Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Keyword(s): Hospital-Acquired Pneumonia, Machine Learning, Ventilator-Associated Pneumonia, Ventilator Bundle, ICU.

Abstract: Pneumonia is the most common infectious disease picked up in the Intensive Care Unit (ICU) and accounts for nearly 27% of all hospital infections—from 5% to 40% of ICU patients on mechanical ventilation risk getting infected by ventilator-associated pneumonia. Fortunately, by identifying patients more likely to contract pneumonia, up to 50% of ventilator-associated pneumonia infections can be avoided. To our knowledge, this is the first study that tackles the problem of identifying ICU patients with a high risk of developing ventilator-associated pneumonia in Saudi hospitals, considering the impact of ventilator bundle compliance rates on the predicted results. Five machine learning models were built using two real life datasets from the Health Electronic Surveillance Network (HESN) at the Saudi Ministry of Health. Results show that including ventilator bundle compliance rates data in the prediction process yields improved results in general; however, the extent of enhancement varies across models. (More)

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Paper citation in several formats:
Alsaab, G. and Alkhodair, S. (2024). Investigating the Impact of Ventilator Bundle Compliance Rates on Predicting ICU Patients with Risk for Hospital-Acquired Ventilator-Associated Pneumonia Infection in Saudi Arabia. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF; ISBN 978-989-758-688-0; ISSN 2184-4305, SciTePress, pages 797-802. DOI: 10.5220/0012574900003657

@conference{healthinf24,
author={Ghaida Alsaab and Sarah Alkhodair},
title={Investigating the Impact of Ventilator Bundle Compliance Rates on Predicting ICU Patients with Risk for Hospital-Acquired Ventilator-Associated Pneumonia Infection in Saudi Arabia},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF},
year={2024},
pages={797-802},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012574900003657},
isbn={978-989-758-688-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF
TI - Investigating the Impact of Ventilator Bundle Compliance Rates on Predicting ICU Patients with Risk for Hospital-Acquired Ventilator-Associated Pneumonia Infection in Saudi Arabia
SN - 978-989-758-688-0
IS - 2184-4305
AU - Alsaab, G.
AU - Alkhodair, S.
PY - 2024
SP - 797
EP - 802
DO - 10.5220/0012574900003657
PB - SciTePress

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