PMID- 35035091 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20220716 IS - 1573-7497 (Electronic) IS - 0924-669X (Print) IS - 0924-669X (Linking) VI - 52 IP - 9 DP - 2022 TI - Rescuing emergency cases of COVID-19 patients: An intelligent real-time MSC transfusion framework based on multicriteria decision-making methods. PG - 9676-9700 LID - 10.1007/s10489-021-02813-5 [doi] AB - Mesenchymal stem cells (MSCs) have shown promising ability to treat critical cases of coronavirus disease 2019 (COVID-19) by regenerating lung cells and reducing immune system overreaction. However, two main challenges need to be addressed first before MSCs can be efficiently transfused to the most critical cases of COVID-19. First is the selection of suitable MSC sources that can meet the standards of stem cell criteria. Second is differentiating COVID-19 patients into different emergency levels automatically and prioritising them in each emergency level. This study presents an efficient real-time MSC transfusion framework based on multicriteria decision-making(MCDM) methods. In the methodology, the testing phase represents the ability to adhere to plastic surfaces, the upregulation and downregulation of specific surface protein markers and finally the ability to differentiate into different kinds of cells. In the development phase, firstly, two scenarios of an augmented dataset based on the medical perspective are generated to produce 80 patients with different emergency levels. Secondly, an automated triage algorithm based on a formal medical guideline is proposed for real-time monitoring of COVID-19 patients with different emergency levels (i.e. mild, moderate, severe and critical) considering the improvement and deterioration procedures from one level to another. Thirdly, a unique decision matrix for each triage level (except mild) is constructed on the basis of the intersection between the evaluation criteria of each emergency level and list of COVID-19 patients. Thereafter, MCDM methods (i.e. analytic hierarchy process [AHP] and vlsekriterijumska optimizcija i kaompromisno resenje [VIKOR]) are integrated to assign subjective weights for the evaluation criteria within each triage level and then prioritise the COVID-19 patients on the basis of individual and group decision-making(GDM) contexts. Results show that: (1) in both scenarios, the proposed algorithm effectively classified the patients into four emergency levels, including mild, moderate, severe and critical, taking into consideration the improvement and deterioration cases. (2) On the basis of experts' perspectives, clear differences in most individual prioritisations for patients with different emergency levels in both scenarios were found. (3) In both scenarios, COVID-19 patients were prioritised identically between the internal and external group VIKOR. During the evaluation, the statistical objective method indicated that the patient prioritisations underwent systematic ranking. Moreover, comparison analysis with previous work proved the efficiency of the proposed framework. Thus, the real-time MSC transfusion for COVID-19 patients can follow the order achieved in the group VIKOR results. CI - (c) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. FAU - Alsalem, M A AU - Alsalem MA AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia. GRID: grid.444506.7. ISNI: 0000 0000 9272 6490 FAU - Albahri, O S AU - Albahri OS AUID- ORCID: 0000-0002-7844-3990 AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia. GRID: grid.444506.7. ISNI: 0000 0000 9272 6490 FAU - Zaidan, A A AU - Zaidan AA AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 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 - Alnoor, Alhamzah AU - Alnoor A AD - School of Management, Universiti Sains Malaysia, 11800 Gelugor, Pulau Pinang Malaysia. GRID: grid.11875.3a. ISNI: 0000 0001 2294 3534 FAU - Alamoodi, A H AU - Alamoodi AH AD - Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia. GRID: grid.444506.7. ISNI: 0000 0000 9272 6490 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 Sultan Idris, Tanjung Malim, Malaysia. GRID: grid.444506.7. ISNI: 0000 0000 9272 6490 FAU - Jumaah, F M AU - Jumaah FM AD - Department of Advanced Applications and Embedded Systems, Intel Corporation, Plot 6, 11900 Bayan Lepas Technoplex, Pulau Pinang Malaysia. LA - eng PT - Journal Article DEP - 20220108 PL - Netherlands TA - Appl Intell (Dordr) JT - Applied intelligence (Dordrecht, Netherlands) JID - 9918284258306676 PMC - PMC8741536 OTO - NOTNLM OT - AHP OT - COVID-19 OT - Mesenchymal stem cell therapy OT - Multicriteria decision-making OT - VIKOR COIS- Competing interestsNo conflict of interest. EDAT- 2022/01/18 06:00 MHDA- 2022/01/18 06:01 PMCR- 2022/01/08 CRDT- 2022/01/17 05:48 PHST- 2021/08/23 00:00 [accepted] PHST- 2022/01/18 06:00 [pubmed] PHST- 2022/01/18 06:01 [medline] PHST- 2022/01/17 05:48 [entrez] PHST- 2022/01/08 00:00 [pmc-release] AID - 2813 [pii] AID - 10.1007/s10489-021-02813-5 [doi] PST - ppublish SO - Appl Intell (Dordr). 2022;52(9):9676-9700. doi: 10.1007/s10489-021-02813-5. Epub 2022 Jan 8.