PMID- 32593060 OWN - NLM STAT- MEDLINE DCOM- 20201109 LR - 20221207 IS - 1872-7565 (Electronic) IS - 0169-2607 (Print) IS - 0169-2607 (Linking) VI - 196 DP - 2020 Nov TI - 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. PG - 105617 LID - S0169-2607(20)31450-4 [pii] LID - 10.1016/j.cmpb.2020.105617 [doi] AB - 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. METHOD: The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix. RESULT: An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments. DISCUSSION: The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector. CI - Copyright (c) 2020. Published by Elsevier B.V. FAU - Albahri, O S AU - Albahri OS AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia. FAU - Al-Obaidi, Jameel R AU - Al-Obaidi JR AD - Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak 35900, Malaysia. FAU - Zaidan, A A AU - Zaidan AA AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia. Electronic address: aws.alaa@gmail.com. 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, B B AU - Zaidan BB AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia. FAU - Salih, Mahmood M AU - Salih MM AD - Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit 34001, Iraq. FAU - Qays, Abdulhadi AU - Qays A AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia. FAU - Dawood, K A AU - Dawood KA AD - Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia. FAU - Mohammed, R T AU - Mohammed RT AD - Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia. FAU - Abdulkareem, Karrar Hameed AU - Abdulkareem KH AD - Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia. FAU - Aleesa, A M AU - Aleesa AM AD - Faculty of Electronic and Electrical Engineering, Universiti Tun Hussein Onn, Batu Pahat, Johor 86400, Malaysia. FAU - Alamoodi, A H AU - Alamoodi AH AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia. FAU - Chyad, M A AU - Chyad MA AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia. FAU - Zulkifli, Che Zalina AU - Zulkifli CZ AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia. LA - eng PT - Journal Article DEP - 20200620 PL - Ireland TA - Comput Methods Programs Biomed JT - Computer methods and programs in biomedicine JID - 8506513 RN - 0 (ABO Blood-Group System) RN - 0 (Antibodies, Viral) RN - 0 (Biomarkers) RN - 0 (Blood Proteins) SB - IM MH - ABO Blood-Group System MH - Antibodies, Viral/blood MH - Betacoronavirus MH - Biomarkers/blood MH - Blood Proteins/analysis MH - COVID-19 MH - Coronavirus Infections/blood/*immunology/*therapy MH - Databases, Factual MH - Decision Making MH - *Decision Support Systems, Clinical MH - Humans MH - Immunization, Passive MH - Machine Learning MH - Pandemics MH - Pneumonia, Viral/blood/*immunology/*therapy MH - SARS-CoV-2 MH - COVID-19 Serotherapy PMC - PMC7305916 OTO - NOTNLM OT - COVID-19 OT - Convalescent plasma therapy OT - MCDM OT - Machine learning OT - Protein biomarker OT - SODOSM OT - Serological COIS- Declaration of Competing Interest None EDAT- 2020/06/28 06:00 MHDA- 2020/11/11 06:00 PMCR- 2020/06/20 CRDT- 2020/06/28 06:00 PHST- 2020/04/24 00:00 [received] PHST- 2020/06/16 00:00 [accepted] PHST- 2020/06/28 06:00 [pubmed] PHST- 2020/11/11 06:00 [medline] PHST- 2020/06/28 06:00 [entrez] PHST- 2020/06/20 00:00 [pmc-release] AID - S0169-2607(20)31450-4 [pii] AID - 105617 [pii] AID - 10.1016/j.cmpb.2020.105617 [doi] PST - ppublish SO - Comput Methods Programs Biomed. 2020 Nov;196:105617. doi: 10.1016/j.cmpb.2020.105617. Epub 2020 Jun 20.