PMID- 34764579 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20230130 IS - 1573-7497 (Electronic) IS - 0924-669X (Print) IS - 0924-669X (Linking) VI - 51 IP - 5 DP - 2021 TI - Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component. PG - 2956-2987 LID - 10.1007/s10489-020-02169-2 [doi] AB - 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 CP-transfusion 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. CI - (c) The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. FAU - Mohammed, Thura J AU - Mohammed TJ AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia. GRID: grid.444506.7. ISNI: 0000 0000 9272 6490 AD - Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq. FAU - Albahri, A S AU - Albahri AS AD - Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq. FAU - Zaidan, A A AU - Zaidan AA AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia. GRID: grid.444506.7. ISNI: 0000 0000 9272 6490 FAU - Albahri, O S AU - Albahri OS AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia. GRID: grid.444506.7. ISNI: 0000 0000 9272 6490 FAU - Al-Obaidi, Jameel R AU - Al-Obaidi JR AD - Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak Malaysia. GRID: grid.444506.7. ISNI: 0000 0000 9272 6490 FAU - Zaidan, B B AU - Zaidan BB AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia. GRID: grid.444506.7. ISNI: 0000 0000 9272 6490 FAU - Larbani, Moussa AU - Larbani M AD - School of Mathematics and Statistics, Carleton University, Ottawa, ON Canada. GRID: grid.34428.39. ISNI: 0000 0004 1936 893X FAU - Mohammed, R T AU - Mohammed RT AD - Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia. GRID: grid.11142.37. ISNI: 0000 0001 2231 800X FAU - Hadi, Suha M AU - Hadi SM AD - Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq. LA - eng PT - Journal Article DEP - 20210122 PL - Netherlands TA - Appl Intell (Dordr) JT - Applied intelligence (Dordrecht, Netherlands) JID - 9918284258306676 PMC - PMC7820530 OTO - NOTNLM OT - AHP OT - COVID-19 OT - Convalescent Plasma OT - Multi-criteria decision making OT - Prioritisation OT - Serological Biomarkers/Protein OT - TOPSIS EDAT- 2021/11/13 06:00 MHDA- 2021/11/13 06:01 PMCR- 2021/01/22 CRDT- 2021/11/12 06:57 PHST- 2020/12/21 00:00 [accepted] PHST- 2021/11/13 06:00 [pubmed] PHST- 2021/11/13 06:01 [medline] PHST- 2021/11/12 06:57 [entrez] PHST- 2021/01/22 00:00 [pmc-release] AID - 2169 [pii] AID - 10.1007/s10489-020-02169-2 [doi] PST - ppublish SO - Appl Intell (Dordr). 2021;51(5):2956-2987. doi: 10.1007/s10489-020-02169-2. Epub 2021 Jan 22.