Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach
The 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)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/19/6379 |
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| author | Mohanad A. Deif Ahmed A. A. Solyman Mohammed H. Alsharif Peerapong Uthansakul |
| author_facet | Mohanad A. Deif Ahmed A. A. Solyman Mohammed H. Alsharif Peerapong Uthansakul |
| author_sort | Mohanad A. Deif |
| collection | DOAJ |
| description | The 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 COVID-19 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. |
| first_indexed | 2024-03-10T06:52:38Z |
| format | Article |
| id | doaj.art-f8bdfc6c57fc4d89a5cfeef1f00de839 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:52:38Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f8bdfc6c57fc4d89a5cfeef1f00de8392023-11-22T16:45:14ZengMDPI AGSensors1424-82202021-09-012119637910.3390/s21196379Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP ApproachMohanad A. Deif0Ahmed A. A. Solyman1Mohammed H. Alsharif2Peerapong Uthansakul3Department of Bioelectronics, Modern University of Technology and Information (MTI), Cairo 11571, EgyptDepartment of Electrical and Electronics Engineering, Istanbul Gelisim University, 34310 Avcılar, TurkeyDepartment of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, KoreaSchool of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandThe 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 COVID-19 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.https://www.mdpi.com/1424-8220/21/19/6379automated triageemergency departmentintensive care admissionsCOVID-19 pandemichybrid XGBoost-AHP approach |
| spellingShingle | Mohanad A. Deif Ahmed A. A. Solyman Mohammed H. Alsharif Peerapong Uthansakul Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach Sensors automated triage emergency department intensive care admissions COVID-19 pandemic hybrid XGBoost-AHP approach |
| title | Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach |
| title_full | Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach |
| title_fullStr | Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach |
| title_full_unstemmed | Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach |
| title_short | Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach |
| title_sort | automated triage system for intensive care admissions during the covid 19 pandemic using hybrid xgboost ahp approach |
| topic | automated triage emergency department intensive care admissions COVID-19 pandemic hybrid XGBoost-AHP approach |
| url | https://www.mdpi.com/1424-8220/21/19/6379 |
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