Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

Basit Öğe Kaydı
dc.authoridhttps://orcid.org/0000-0002-4388-1480en_US
dc.authoridhttps://orcid.org/0000-0002-2881-8635en_US
dc.authoridhttps://orcid.org/0000-0001-8579-5444en_US
dc.authoridhttps://orcid.org/0000-0002-7108-9263en_US
dc.contributor.authorDeif, Mohanad A.
dc.contributor.authorSolyman, Ahmad Amin Ahmad
dc.contributor.authorAlsharif, Mohammed H.
dc.contributor.authorUthansakul, Peerapong
dc.date.accessioned2023-08-10T19:32:29Z
dc.date.available2023-08-10T19:32:29Z
dc.date.issued2021en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractThe sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients’ priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions’ priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley’s Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians’ decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies.en_US
dc.identifier.doi10.3390/s21196379en_US
dc.identifier.endpage17en_US
dc.identifier.issn1424-8220
dc.identifier.issue19en_US
dc.identifier.pmid34640700en_US
dc.identifier.scopus2-s2.0-85115608104en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/11363/5237
dc.identifier.urihttps://doi.org/
dc.identifier.volume21en_US
dc.identifier.wosWOS:000710374100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorSolyman, Ahmad Amin Ahmad
dc.language.isoenen_US
dc.publisherMDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLANDen_US
dc.relation.ispartofSensorsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectautomated triageen_US
dc.subjectemergency departmenten_US
dc.subjectintensive care admissionsen_US
dc.subjectCOVID-19 pandemicen_US
dc.subjecthybrid XGBoost-AHP approachen_US
dc.titleAutomated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approachen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
sensors-21-06379-v3.pdf
Boyut:
9.21 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale / Article
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.56 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Web of Science ve Scopus Atıf Dizinlerindeki Yayınlar
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu