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10.1016/j.cmpb.2020.105617 http://scihub22266oqcxt.onion/10.1016/j.cmpb.2020.105617 32593060!7305916!32593060     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 298.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 298.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 298.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 298.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 298.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 298.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 298.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 298.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 332.4 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 332.4 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Methods+Programs+Biomed 2020 ; 196 (ä): 105617 Nephropedia Template TP {{tp|p=32593060|t=2020. 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 |pdf=|usr=}}{{32593060}} gab.com Text 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 JO: Comput Methods Programs Biomed http://www.kidney.de/pget.php?&tw=1&pmid=32593060 #FOAvip 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. Twit Text FOAVip 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 ????Comput Methods Programs Biomed http://www.kidney.de/pget.php?&tw=1&pmid=32593060 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32593060 #FOAvip English Wikipedia <ref>{{Cite pmid|32593060}}</ref> 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 #MMPMID32593060 Albahri OS; Al-Obaidi JR; Zaidan AA; Albahri AS; Zaidan BB; Salih MM; Qays A; Dawood KA; Mohammed RT; Abdulkareem KH; Aleesa AM; Alamoodi AH; Chyad MA; Zulkifli CZ Comput Methods Programs Biomed 2020[Nov]; 196 (ä): 105617 PMID32593060show ga 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. |*Decision Support Systems, Clinical[MESH] |ABO Blood-Group System[MESH] |Antibodies, Viral/blood[MESH] |Betacoronavirus[MESH] |Biomarkers/blood[MESH] |Blood Proteins/analysis[MESH] |COVID-19[MESH] |COVID-19 Serotherapy[MESH] |Coronavirus Infections/blood/*immunology/*therapy[MESH] |Databases, Factual[MESH] |Decision Making[MESH] |Humans[MESH] |Immunization, Passive[MESH] |Machine Learning[MESH] |Pandemics[MESH] |Pneumonia, Viral/blood/*immunology/*therapy[MESH]Helping doctors hasten COVID-19 treatment: Towards a rescue framework (epmc).pdf DeepDyve Pubget Overpricing