Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods

Albahri, O. S.; Al‐Obaidi, Jameel R.; Zaidan, A. A.; Albahri, A. S.; Zaidan, B. B.; Salih, Mahmood Maher; Qays, Abdulhadi; Dawood, Kareem Abbas; Mohammed, R. T.; Abdulkareem, Karrar Hameed; Aleesa, Ahmed Marwan; Alamoodi, A. H.; Chyad, M. A.; Zulkifli, Che Zalina

doi:10.1016/j.cmpb.2020.105617

Cited by 87 publications

(93 citation statements)

References 99 publications

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“…Second, a multi-criteria decision making process is involved in the selection of the most suitable plasma and prioritization of patients. In [54] , a machine learning algorithm is used in the decision making process.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

148

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Section: Clinical Applicationsmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

148

View full text Add to dashboard Cite

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“…Albahri et al [120] also proposed an ML-based rescue framework that was combined with a novel multi-criteria decision-making (MCDM) model to identify and prioritize severely infected COVID-19 patients who are most suitable for relevant convalescent plasma (CP) transfusion.…”

Section: Ai-based Approaches For Covid-19mentioning

confidence: 99%

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

Rasheed

Jamıl

Hameed

et al. 2020

Chaos, Solitons & Fractals

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Highlights The current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak. This paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. Investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. The impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. The investigation of this paper reveals several AI-based approaches that have been proposed as potential ways to help, with the COVID-19 pandemic, covering everything from initial diagnoses via image diagnostics up to the presentation of models that help to understand the spread of COVID-19 and identify potential new outbreak areas.

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“…CP transfusion to COVID-19 patients, which is considered as one of the most successful protocols, is used in hospitals to treat this disease [ 11 , 12 ]. Moreover, integration amongst hospitals in terms of the intelligent transfusion of CP across centralised/decentralised telemedicine architecture is necessary to help doctors in the rapid delivery of COVID-19 treatment [ 10 ]. For a clear view on how to support the hospital community in managing a CP-transfusion-rescue intelligent framework across the centralised/decentralised telemedicine architecture for the COVID-19 pandemic, five sequential questions are raised and answered as follows.…”

Section: Introductionmentioning

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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Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods

Cited by 87 publications

References 99 publications

Applications of artificial intelligence in battling against covid-19: A literature review

Applications of artificial intelligence in battling against covid-19: A literature review

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

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

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