Based Multiple Heterogeneous Wearable Sensors: A Smart Real-Time Health Monitoring Structured for Hospitals Distributor

Computer Methods and Programs in Biomedicine

et al. 2020

Self Cite

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.

Section: Development Of Prioritisation Approach Via Mcdm Methodsmentioning

confidence: 99%

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

Al‐Obaidi

Computer Methods and Programs in Biomedicine

et al. 2020

Self Cite

“…MCDM is an extension of the decision theory that covers any multi-objective decision and solves numerous problems either for healthcare or other domains. A decision matrix is constructed on the basis of an intersection between the evaluation criteria used and rank/prioritise a set of alternatives based on the evaluation criteria [ 1 , 2 , 5 , 14 , 15 , 23 , 24 , 26 , 37 , 45 , 52 , 59 – 61 , 87 , 120 , 121 , 123 ]. Then, patients with emergency cases are classified based on their blood type to be matched with the list of tested donor CPs, which are prioritised using the constructed CP decision matrix (refer to [ 19 ] for more details).…”

Section: Introductionmentioning

confidence: 99%

PSO–Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture

Mohsin

et al. 2021

Multimed Tools Appl

Self Cite

Secure updating and sharing for large amounts of healthcare information (such as medical data on coronavirus disease 2019 ) in efficient and secure transmission are important but challenging in communication channels amongst hospitals. In particular, in addressing the above challenges, two issues are faced, namely, those related to confidentiality and integrity of their health data and to network failure that may cause concerns about data availability. To the authors' knowledge, no study provides secure updating and sharing solution for large amounts of healthcare information in communication channels amongst hospitals. Therefore, this study proposes and discusses a novel steganographybased blockchain method in the spatial domain as a solution. The novelty of the proposed method is the removal and addition of new particles in the particle swarm optimisation (PSO) algorithm. In addition, hash function can hide secret medical COVID-19 data in hospital databases whilst providing confidentiality with high embedding capacity and high image quality. Moreover, stego images with hash data and blockchain technology are used in updating and sharing medical COVID-19 data between hospitals in the network to improve the level of confidentiality and protect the integrity of medical COVID-19 data in grey-scale images, achieve data availability if any connection failure occurs in a single point of the network and eliminate the central point (third party) in the network during transmission. The proposed method is discussed in three stages. Firstly, the pre-hiding stage estimates the embedding capacity of each host image. Secondly, the secret COVID-19 data hiding stage uses PSO algorithm and hash function. Thirdly, the transmission stage transfers the stego images based on blockchain technology and updates all nodes (hospitals) in the network. As proof of concept for the case study, the authors adopted the latest COVID-19 research published in the Computer Methods and Programs in Biomedicine journal, which presents a rescue framework within hospitals for the storage and transfusion of the best convalescent plasma to the most critical patients with COVID-19 on the basis of biological requirements. The validation and evaluation of the proposed method are discussed.

“…[36][37][38][39] For the second issue, di®erent weights are often given for the mentioned attributes, which further increase task complexity. [40][41][42][43]98 Finally, within the optimal range, C1 at a low reference range and other factors (C2-C8) at a high reference range have a signi¯cant e®ect in the selection of the best healthy patients. [44][45][46][47] This inverse relationship between attributes causes a trade-o®, that is, the third issue.…”

Section: Introductionmentioning

confidence: 99%

“…[61][62][63][64][65]92,101 The combination of the analytic hierarchy process (AHP) and VIekriterijumsko KOmpromisno Rangiranje (VIKOR) in one platform is recommended in the medical domain. 26,66,91 The basis of integration is formed when the weights for the criteria are allocated in accordance with AHP. VIKOR is suggested for prioritizing patients with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods

Albahri

Al‐Obaidi

Int. J. Info. Tech. Dec. Mak.

et al. 2020

Self Cite

Coronavirus disease (COVID-19) pandemic has a tremendous effect on people’s lives worldwide, and the number of infected patients increases daily. The healthcare sector is affected by a large number of patients with COVID-19, and a solution is urgently needed to avert the risk of deteriorating patients in terms of prioritizing patients based on their health conditions. Prioritization of patients with COVID-19 is a complex and multi-criteria decision-analysis (MCDA) problem due to (i) multiple biological laboratory examination criteria, (ii) criteria importance and (iii) trade-off amongst the criteria. This study presents a new multi-biological laboratory examination framework for prioritizing patients with COVID-19 on the basis of integrated MCDA methods. The experiment was conducted on the basis of three phases. In the first phase, patient datasets containing eight biological laboratory examination criteria for six patients with COVID-19 were derived and discussed. The outcome of this phase was used to propose a decision matrix on the basis of the intersection between “biological laboratory examination criteria” and “COVID-19 patients list”. In the second phase, the analytic hierarchy process (AHP) method was utilized to set the subjective weights for the biological laboratory examination criteria by respiratory experts. In the last phase, the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method was adopted to prioritize patients in the context of individual and group decision making (GDM). Results showed that (1) the integration of AHP–VIKOR method based on individual and GDM contexts was effective for solving prioritization problems for patients with COVID-19, and (2) the prioritization results of patients with COVID-19 showed no variation in the internal and external VIKOR GDM contexts. The proposed multi-biological laboratory examination framework can differentiate between the mild and serious or critical condition of patients with COVID-19 by prioritizing them based on integrated AHP–VIKOR methods. In conclusion, medical sectors can use the proposed framework to differentiate the health conditions of infected patients and to assign appropriate care with prompt and effective treatment.