Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

Albahri, A. S.; Hamid, Rula A.; Albahri, O. S.; Zaidan, A. A.

doi:10.1016/j.artmed.2020.101983

Cited by 88 publications

(56 citation statements)

References 81 publications

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“…TOPSIS for the prioritisation of COVID-19 patients/donors In this stage, TOPSIS is used to prioritise COVID-19 patients/ donors based on the weighted criteria from the AHP method to tackle the major weakness of TOPSIS, which is the lack of provision weight for the evaluation criteria [88][89][90][91]. In general, the evaluation criteria can be classified into two types: benefit and cost [92,93]. Benefit criterion indicates that a larger value is more valuable, whereas cost criteria are the opposite.…”

Section: C-reactive Protein Mg/l (<8)mentioning

confidence: 99%

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

et al. 2021

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As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CPtransfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified 'as a proof of concept'. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five 'serological/protein biomarker' criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.

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Section: C-reactive Protein Mg/l (<8)mentioning

confidence: 99%

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

et al. 2021

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“…MCDM methods have been extensively used in emergency decision‐making problems and recently applied in taking decisions against COVID‐19 pandemics 29–33 . Furthermore, locating regular and field hospitals has been extensively studied in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

Determining the location of isolation hospitals for COVID‐19 via Delphi‐based MCDM method

Aydın

Şeker

2021

Int J Intell Syst

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The existing contagious epidemic disease, [SARS]‐CoV‐2, has been one of the biggest public health problems that humankind combatting against since December 2019. Answering the increase in the number of infected patients during the pandemic is one of the biggest challenges for healthcare systems, where resources have already been employed by a significant number of patients. While assigning most of the resources to infected people is an effective way in a short‐term planning, its bitter effects on regular healthcare cannot be undervalued. Moreover, within this plan the risk of spreading the disease to other patients and healthcare providers is another risk that should not be underestimated. Therefore, in this study, we proposed the Delphi‐based multicriteria decision‐making (MCDM) framework for selecting the most appropriate location for an isolation hospital serving only epidemic‐based patients with mild to moderate symptoms. The integrated framework consists of Delphi, Best–Worst Method, and interval type‐2 fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies. Nine most effecting criteria are considered in the evaluation of five alternative locations in a real case study conducted at the European side of Istanbul. Ataturk Airport is determined as the best location to set up an isolation hospital based on determined nine evaluation criteria. The effectiveness and robustness of the framework are analyzed through comparative and sensitivity analyses.

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“…The MCDM evaluates alternatives by integrating individual criteria that are often conflicting into a comprehensive evaluation 44–48 . Decision‐making techniques are gaining wide attention, of which the MCDM is the most vital 49–53 .…”

Section: Introductionmentioning

confidence: 99%

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Krishnan

Mohammed

Alnoor

et al. 2021

Int J of Intelligent Sys

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The benchmarking of smart e‐tourism data management applications falls under the problem of multicriteria decision‐making (MCDM). This claim is supported by three issues: 12 smart key concepts need to be considered in the evaluation, criteria importance, and data variation among these criteria. Thus, an MCDM solution is essential to overcome problem complexity. To end this, this study presents a decision‐making framework on the basis of the extension of interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency (IT2TR‐FWZIC) integrated with the Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method for evaluating and benchmarking the smart e‐tourism data management applications. Our methodology comprises two consecutive phases. In the first phase, a decision matrix is constructed using the intersection between the 12 key concepts and smart e‐tourism data management applications of each category and subcategory in smart e‐tourism. In the second phase, the integration of the IT2TR‐FWZIC formulation and VIKOR is presented to compute the weights for the 12 key concepts and benchmark the smart e‐tourism data management applications for each category. The results are as follows: (1) A clear difference is found among the criteria weights (12 smart key concepts). Specifically, the real‐time criterion achieves the highest importance weight (0.098), whereas augmented reality obtains the lowest weight (0.068). The context‐awareness and recommender systems have the same weight value (0.087), and the other eight criteria are distributed in between. (2) The smart e‐tourism data management applications are evaluated and benchmarked effectively per category and subcategories. (3) Benchmarked applications in each category are subjected to a systematic ranking in the evaluation process. The sensitivity analysis has shown high correlation outcomes to the systematic ranking results over the 31 scenarios of criteria weight changing. Moreover, a comparative analysis of the proposed work with other existing studies is also discussed.

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Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

Cited by 88 publications

References 81 publications

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

Determining the location of isolation hospitals for COVID‐19 via Delphi‐based MCDM method

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

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