•Intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19.•ABO compatibility is assessed after classifying donors into the ...four blood types.•Contracted patient decision matrix in-between‘serological/protein biomarkers and the PaO2/FiO2’ and ‘patients list’.•Proposed a novel subjective and objective decision by opinion score method (SODOSM)’.•Contracted CP-decision matrix in-between serological/protein bio-markers criteria and ‘CPs tested list’.
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.
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.
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.
An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments.
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.
•A novel technique for reorganisation of opinion order to interval levels (TROOIL) is presented.•A smart real-time remote health-monitoring framework is developed on the basis of TROOIL technique to ...prioritise patients with multiple chronic diseases (MCDs).•The prioritisation of patients with MCDs is validated objectively.•Different scenarios are provided to evaluate the proposed framework.
Telemedicine has been increasingly used in healthcare to provide services to patients remotely. However, prioritising patients with multiple chronic diseases (MCDs) in telemedicine environment is challenging because it includes decision-making (DM) with regard to the emergency degree of each chronic disease for every patient.
This paper proposes a novel technique for reorganisation of opinion order to interval levels (TROOIL) to prioritise the patients with MCDs in real-time remote health-monitoring system.
The proposed TROOIL technique comprises six steps for prioritisation of patients with MCDs: (1) conversion of actual data into intervals; (2) rule generation; (3) rule ordering; (4) expert rule validation; (5) data reorganisation; and (6) criteria weighting and ranking alternatives within each rule. The secondary dataset of 500 patients from the most relevant study in a remote prioritisation area was adopted. The dataset contains three diseases, namely, chronic heart disease, high blood pressure (BP) and low BP.
The proposed TROOIL is an effective technique for prioritising patients with MCDs. In the objective validation, remarkable differences were recognised among the groups’ scores, indicating identical ranking results. In the evaluation of issues within all scenarios, the proposed framework has an advantage of 22.95% over the benchmark framework.
Patients with the most severe MCD were treated first on the basis of their highest priority levels. The treatment for patients with less severe cases was delayed more than that for other patients.
The proposed TROOIL technique can deal with multiple DM problems in prioritisation of patients with MCDs.
Secure updating and sharing for large amounts of healthcare information (such as medical data on coronavirus disease 2019 COVID-19) 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 steganography-based 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.
•Formulated an extension of FWZIC method under fuzzy environment q-ROFS were called q-ROFWZIC.•Formulated an extension of FDOSM under fuzzy environment q-ROFS were called q-ROFDOSM.•Developed an ...integration between q-ROFWZIC method and q-ROFDOSM.•Performed the proposed Integration Methods on a Case Study of COVID-19 Vaccine Distribution
Owing to the limitations of Pythagorean fuzzy and intuitionistic fuzzy sets, scientists have developed a distinct and successive fuzzy set called the q-rung orthopair fuzzy set (q-ROFS), which eliminates restrictions encountered by decision-makers in multicriteria decision making (MCDM) methods and facilitates the representation of complex uncertain information in real-world circumstances. Given its advantages and flexibility, this study has extended two considerable MCDM methods the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) under the fuzzy environment of q-ROFS. The extensions were called q-rung orthopair fuzzy-weighted zero-inconsistency (q-ROFWZIC) method and q-rung orthopair fuzzy decision by opinion score method (q-ROFDOSM). The methodology formulated had two phases. The first phase ‘development’ presented the sequential steps of each method thoroughly.The q-ROFWZIC method was formulated and used in determining the weights of evaluation criteria and then integrated into the q-ROFDOSM for the prioritisation of alternatives on the basis of the weighted criteria. In the second phase, a case study regarding the MCDM problem of coronavirus disease 2019 (COVID-19) vaccine distribution was performed. The purpose was to provide fair allocation of COVID-19 vaccine doses. A decision matrix based on an intersection of ‘recipients list’ and ‘COVID-19 distribution criteria’ was adopted. The proposed methods were evaluated according to systematic ranking assessment and sensitivity analysis, which revealed that the ranking was subject to a systematic ranking that is supported by high correlation results over different scenarios with variations in the weights of criteria.
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•The introduction of an alternative approach for HIE.•The new idea as a framework with overall architecture and individual components.•The complete design specifications of the ...proposed framework.•Proof-of-concept prototype to validate the idea and show possible implementation.
Nationwide health information exchange (NHIE) continues to be a persistent concern for government agencies, despite the many efforts and the conceived benefits of sharing patient data among healthcare providers. Difficulties in ensuring global connectivity, interoperability, and concerns on security have always hampered the government from successfully deploying NHIE. By looking at NHIE from a fresh perspective and bearing in mind the pervasiveness and power of modern mobile platforms, this paper proposes a new approach to NHIE that builds on the notion of consumer-mediated HIE, albeit without the focus on central health record banks. With the growing acceptance of smartphones as reliable, indispensable, and most personal devices, we suggest to leverage the concept of mobile personal health records (PHRs installed on smartphones) to the next level. We envision mPHRs that take the form of distributed storage units for health information, under the full control and direct possession of patients, who can have ready access to their personal data whenever needed. However, for the actual exchange of data with health information systems managed by healthcare providers, the latter have to be interoperable with patient-carried mPHRs. Computer industry has long ago solved a similar problem of interoperability between peripheral devices and operating systems. We borrow from that solution the idea of providing special interfaces between mPHRs and provider systems. This interface enables the two entities to communicate with no change to either end. The design and operation of the proposed approach is explained. Additional pointers on potential implementations are provided, and issues that pertain to any solution to implement NHIE are discussed.
This study formulated a new version of FWZIC for weighting the security and privacy properties, that is, spherical FWZIC (S-FWZIC). Moreover, an integrated MCDM framework was developed for ...benchmarking blockchain-based IoT healthcare Industry 4.0 systems on the basis of multi security and privacy properties. In the first phase of the methodology, a decision matrix is formulated based on the intersection of blockchain-based Internet of Things healthcare Industry 4.0 systems and security and privacy properties (i.e. user authentication, access control, privacy protection, integrity availability and anonymity). In the second phase, the weights of each security and privacy property are calculated through the S-FWZIC method. Then, these weights are employed to benchmark blockchain-based IoT healthcare Industry 4.0 systems through the combined GRA-TOPSIS and BES optimisation method.
This study reviews and analyses the research landscape for intrusion detection systems (IDSs) based on deep learning (DL) techniques into a coherent taxonomy and identifies the gap in this pivotal ...research area. The focus is on articles related to the keywords ‘deep learning’, ‘intrusion’ and ‘attack’ and their variations in four major databases, namely Web of Science, ScienceDirect, Scopus and the Institute of Electrical and Electronics Engineers’
Xplore
. These databases are sufficiently broad to cover the technical literature. The dataset comprises 68 articles. The largest proportion (72.06%; 49/68) relates to articles that develop an approach for evaluating or identifying intrusion detection techniques using the DL approach. The second largest proportion (22.06%; 15/68) relates to studying/applying articles to the DL area, IDSs or other related issues. The third largest proportion (5.88%; 4/68) discusses frameworks/models for running or adopting IDSs. The basic characteristics of this emerging field are identified from the aspects of motivations, open challenges that impede the technology’s utility, authors’ recommendations and substantial analysis. Then, a result analysis mapping for new directions is discussed. Three phases are designed to meet the demands of detecting distributed denial-of-service attacks with a high accuracy rate. This study provides an extensive resource background for researchers who are interested in IDSs based on DL.
Theoretical models have become increasingly complex, but the dual-phase structural equation modelling (SEM) and artificial neural network analysis can be used by scholars to unveil the causal ...interactions and nonlinear relationships between variables. However, not only a single open issue and challenge—but several of them—are encountered in the use of different multi-assessment types of measurement model to achieve the reliability and validity whilst implementing SEM, but the gaps have not been fully determined at present. The issues significantly impact the effectiveness process of selecting the most suitable method to assess the measurement model of SEM. Once the best sequence quality improvement is met, it then needs to present a recommendable solution. To this end, this study completes the literature by presenting a systematic review of all main advanced aspects of the SEM reliability and validity approaches. Firstly, the databases of ScienceDirect, IEEE Xplore, Web of Science and Scopus were checked for the retrospective studies. A total of 239 papers were gathered for the period covering 2016 to June 2021. Then, the obtained articles were filtered according to the predefined inclusion criteria. Sixty articles were ultimately selected and divided into three categories (single, hybrid and other types) to enable a new representation of the crossover taxonomy amongst ‘SEM reliability and validity’ and ‘multi-assessment methods for structural model’ for the first time. The three categories had been matched with the SEM processes, and each of the detailed models were defined to determine the sets of principal criteria of the entire selected SEM approaches. Consequently, this multi-field interdisciplinary review was used to expose the state-of-the-art challenges and open issues (i.e. multiple-evaluation criteria, importance criteria and data variation) related to the sets of SEM criteria necessitating a selection process for deriving the best SEM method. Each issue entailed a ‘wherefore’, and multi-criteria decision making was adopted to handle the complexity problems in the different cases. Thus, a new three-phase decision-making methodology was constructed. In the first phase, a decision matrix (DM) was identified for the SEM approach; the composition of the decision alternatives and identified criteria were derived from the academic literature. In the second phase, the development methodology was achieved on the basis of the integrated multi-criteria DM techniques. The analytic hierarchy process was used for the subjective weighting of the criteria within the constructed DM, whereas the vlsekriterijumska optimizcija i kaompromisno resenje technique was used for ranking and selecting the best SEM methods. In the third phase, an objective validation approach was adopted to validate the proposed methodology. The outcome of this novel approach is intended to guide decision makers and policymakers on the easy evaluation of their goals of selecting the most suitable computing methods and the improvement of the reliability and validity of SEM.
The main objective of this study is to propose a novel verification secure framework for patient authentication between an access point (patient enrolment device) and a node database. For this ...purpose, two stages are used. Firstly, we propose a new hybrid biometric pattern model based on a merge algorithm to combine radio frequency identification and finger vein (FV) biometric features to increase the randomisation and security levels in pattern structure. Secondly, we developed a combination of encryption, blockchain and steganography techniques for the hybrid pattern model. When sending the pattern from an enrolment device (access point) to the node database, this process ensures that the FV biometric verification system remains secure during authentication by meeting the information security standard requirements of confidentiality, integrity and availability. Blockchain is used to achieve data integrity and availability. Particle swarm optimisation steganography and advanced encryption standard techniques are used for confidentiality in a transmission channel. Then, we discussed how the proposed framework can be implemented on a decentralised network architecture, including access point and various databases node without a central point. The proposed framework was evaluated by 106 samples chosen from a dataset that comprises 6000 samples of FV images. Results showed that (1) high-resistance verification framework is protected against spoofing and brute-force attacks; most biometric verification systems are vulnerable to such attacks. (2) The proposed framework had an advantage over the benchmark with a percentage of 55.56% in securing biometric templates during data transmission between the enrolment device and the node database.
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