Context: People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19. Objective: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

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.

Along with the developments of numerous MaOO algorithms in the last decades, comparing the performance of MaOO algorithms with one another is also highly needed. Many studies have attempted to manipulate such comparison to analyze the performance quality of MaOO. In such cases, the weight of importance is critical for evaluating the performance of MaOO algorithms. All evaluation studies for MaOO algorithms have ignored to assign such weight for the target criteria during evaluation process, which plays a key role in the final decision results. Therefore, the weight value of each criterion must be determined to guarantee the accuracy of results in the evaluation process. Multicriteria decision-making (MCDM) methods are extremely preferred in solving weighting issues in the evaluation process of MaOO algorithms. Several studies in MCDM have proposed competitive weighting methods. However, these methods suffer from inconsistency issues arising from the high subjectivity of pairwise comparison. The inconsistency rate increases in an exorbitant manner when the number of criteria increases, and the final results are affected. The primary objective of this study is to propose a new method, called a Novel Fuzzy-Weighted Zero-Inconsistency (FWZIC) Method which can determine the weight coefficients of criteria with zero consistency. This method depends on differences in the preference of experts per criterion to compute its significance level in the decision-making process. The proposed FWZIC method comprises five phases for determining the weights of the evaluation criteria: (1) the set of evaluation criteria is explored and defined, (2) the structured expert judgement (SEJ) is used, (3) the expert decision matrix (EDM) is built on the basis of the crossover of criteria and SEJ, (4) a fuzzy membership function is applied to the result of the EDM and (5) the final values of the weight coefficients of the evaluation criteria are computed. The proposed method is applied to the evaluation criteria of MaOO competitive algorithms. The case study consists of more than 50 items distributed amongst the major criteria, subcriteria and indicators. The significant contribution of each item to the algorithm evaluation is determined. Results show that the criteria, subcriteria and their related indicators are weighted without inconsistency. The findings clearly show that the FWZIC method can deal with the inconsistency issue and provide accurate weight values to each criterion.

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