Evaluation and Benchmarking Trauma Patients in Intensive Care Units Using a Novel Decision-Making Framework

A. S. Albahri, Mohammed S. Al-Samarraay, O. S. Albahri, A. H. Alamoodi, Rula A. Hamid, Raad Z. Homod, Saleh Mahdi Mohammed and Iman Mohamad Sharaf
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A. S. Albahri: Technical Engineering College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq†University of Information Technology and Communications (UOITC), Baghdad, Iraq
Mohammed S. Al-Samarraay: ��Electrical and Electronic Engineering Department, College of Engineering, Gulf University, Sanad 26489, Bahrain
O. S. Albahri: �Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, Iraq¶Institute of Innovation, Science and Sustainability (IISS), Federation University, Melbourne Campus, Australia
A. H. Alamoodi: ��Applied Science Research Center, Applied Science Private University, Amman, Jordan**GUST Engineering and Applied Innovation Research Center (GEAR), Gulf University for Science and Technology, Mishref, Kuwait
Rula A. Hamid: ��†College of Business Informatics, University of Information, Technology and Communications (UOITC), Baghdad, Iraq
Raad Z. Homod: ��‡Department of Oil and Gas Engineering, Basra University of Oil and Gas, Basra 1004, Iraq
Saleh Mahdi Mohammed: Technical Engineering College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
Iman Mohamad Sharaf: �§Higher Technological Institute, Tenth of Ramadan City, Egypt

International Journal of Information Technology & Decision Making (IJITDM), 2025, vol. 24, issue 07, 2005-2041

Abstract: Trauma patients often face complex health consequences and are at a risk of rapid deterioration. Prioritizing trauma patients in intensive care units (ICUs) is essential for timely intervention. Current prioritization approaches are challenging tasks that involve considering multiple evaluation criteria, trade-offs and criteria importance, thus requiring a robust multicriteria decision-making (MCDM) approach. In this study, we propose a novel MCDM framework for the evaluation and benchmarking of trauma patients on the basis of health control (HC) criteria derived from real trauma data. The framework consists of two main phases. In the first phase, a dataset of adult trauma patients referred to a trauma network in the West Midlands region of the United Kingdom is identified and preprocessed. This dataset covers the period from 15 May 2014 to 16 December 2016, providing continuous monitoring of the patients. In the second phase, MCDM methods are employed to develop a dynamic decision matrix (DM) that assesses 35 trauma patients on the basis of 16 HC trauma criteria. Using 2-tuple spherical fuzzy linguistic numbers with fuzzy-weighted zero-inconsistency (2TSFLNs-FWZIC), the framework ensures accurate weighting of the criteria, with C1=PS14 and C5=NISS receiving the highest weight values (0.062606) and C2=CD3+8+(106/L) receiving the lowest weight value (0.062353). This weighting process is guided by the input and expertise of a panel of five emergency medicine specialists who have experience in trauma patient management. The results indicate that CoCoSo effectively ranks patients from the least critical (k = 1.348725) to the most critical cases (k = 1.622053) for ICU admission. The proposed framework is evaluated via systematic ranking and sensitivity analysis, providing validation measures for its performance, robustness and reliability.

Keywords: Trauma; multicriteria decision-making; combined compromise solution; fuzzy-weighted zero inconsistency; 2-tuple spherical fuzzy linguistic numbers (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219622025500282

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