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Alnaeem MM, Banihani SS, Islaih A, Al-Qudimat AR. Expectations of emergency patients regarding triage system knowledge upon arrival: an interpretive study. Ir J Med Sci 2024:10.1007/s11845-024-03706-5. [PMID: 38739348 DOI: 10.1007/s11845-024-03706-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 05/07/2024] [Indexed: 05/14/2024]
Abstract
BACKGROUND One of the most important aspects of healthcare knowledge is having a thorough understanding of the triage system which is used in emergency departments. This study aims to assess the level of awareness of Jordanian patients who visit the ED about the triage procedure. METHODS A descriptive, cross-sectional design was utilized in the emergency department at the biggest public hospital in Jordan. A convenience sample of a self-administrated questionnaire utilizing a Discounted Cash Flow Interview (DCF) survey was filled out. RESULTS A total of 726 participants were recruited with a response rate of 90.8%. The mean age of the participants was M = 38.1 (SD = 12.9), and the age of the participants varied from 18 to 89 years. More than half of the participants were male (n = 383, 52.8%) and married (n = 425, 58.5%). A significant relationship between the overall perception of knowing what a teaching hospital is and patients' educational level (X2 = 11.9, P < 0.003), current job (X2 = 25.2, P < 0.001), nationality (X2 = 7.20, P < 0.007), and family income (X2 = 15.9, P < 0.001). CONCLUSION More investigation is required to determine the causes of the low knowledge of the triage system. The study suggests increasing staffing levels, giving nursing staff ongoing education and training, and integrating technology and automation to reduce the load of patient care.
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Affiliation(s)
| | | | - Asma Islaih
- School of Nursing, Al-Zaytoonah University of Jordan, Amman, Jordan
| | - Ahmad R Al-Qudimat
- Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha, Qatar.
- Department of Public Health, College of Health Sciences, QU-Health, Qatar University, Doha, Qatar.
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Ghailani H, Zaidan A, Qahtan S, Alsattar HA, Al-Emran M, Deveci M, Delen D. Developing sustainable management strategies in construction and demolition wastes using a q-rung orthopair probabilistic hesitant fuzzy set-based decision modelling approach. Appl Soft Comput 2023; 145:110606. [DOI: 10.1016/j.asoc.2023.110606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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3
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Qahtan S, Alsattar HA, Zaidan A, Deveci M, Pamucar D, Delen D, Pedrycz W. Evaluation of agriculture-food 4.0 supply chain approaches using Fermatean probabilistic hesitant-fuzzy sets based decision making model. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
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4
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Structural Elements and Requirements in Forming Prehospital Health Response Teams in Response to Chemical, Biological, Radiation, and Nuclear Incidents (CBRN), a Comparative Review Study. Disaster Med Public Health Prep 2023; 17:e300. [PMID: 36785533 DOI: 10.1017/dmp.2022.259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
One of the important indicators of increasing the capacity of the health system and the chances of survival of patients and injured immediately after chemical, biological, radiation and nuclear (CBRN) accidents is rapid access to medical services. Establishing prehospital health response teams is one of the main strategies to improve the capacity and ability to respond to unusual events. The aim of this study was to investigate the factors influencing the formation of rapid response teams in the field of health in response to chemical, biological, radiation and nuclear accidents (CBRN EDMRT). In this study, the comparative review method was used. The study period was from November 1, 2021 to March 2022. Forming and deploying rapid health response teams based on an extensive multi-step search and keywords in multiple databases such as PubMed, CINHAL, Blackwell, Iranmedex, SID, Cochrane Database of Systematic Reviews, Google Scholar, Scopus Also, the websites of the Ministry of Health and the responsible organizations in different countries and the proposed structure were done by international institutions and sites. After accessing the resources and documents, the process of analysis and comparison of different team structures was performed. After the initial search, the structure and required elements of their teams were extracted. According to published articles and texts, 10 teams from the International Atomic Energy Agency (IAEA), the US Centers for Disease Control and Prevention (CDC), the US Department of Homeland Security, and the North Atlantic Treaty Organization (NATO), Australia, the British Public Health Organization, and the Japanese Red Cross were compared. Team requirements, population distribution, type of accident, level of team activity and training, equipment required by the team after the accident, according to which, each country/organization should consider the above factors to design and establish the structure of CBRN EDMRT to take. A study should be conducted to design a comprehensive and evidence-based structure.
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5
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Kuhn E, Henke O, Evang E, Falkenberg T, Bruchhausen W, Schultz A. Silent Triage: Public Health decision-making beyond prioritisation. BMJ Glob Health 2023; 8:bmjgh-2022-011376. [PMID: 36813304 PMCID: PMC9950902 DOI: 10.1136/bmjgh-2022-011376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 02/05/2023] [Indexed: 02/24/2023] Open
Affiliation(s)
- Eva Kuhn
- Section Global Health, Institute for Hygiene and Public Health, University Hospital Bonn, Bonn, Germany
| | - Oliver Henke
- Section Global Health, Institute for Hygiene and Public Health, University Hospital Bonn, Bonn, Germany
| | - Esther Evang
- Section Global Health, Institute for Hygiene and Public Health, University Hospital Bonn, Bonn, Germany
| | - Timo Falkenberg
- Institute for Hygiene and Public Health, University Hospital Bonn, Bonn, Germany
| | - Walter Bruchhausen
- Section Global Health, Institute for Hygiene and Public Health, University Hospital Bonn, Bonn, Germany
| | - Andreas Schultz
- Section Global Health, Institute for Hygiene and Public Health, University Hospital Bonn, Bonn, Germany
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Sustainability assessment of palm oil industry 4.0 technologies in a circular economy applications based on interval-valued Pythagorean fuzzy rough set-FWZIC and EDAS methods. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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7
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Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment. Neural Comput Appl 2023; 35:6185-6196. [PMID: 36415285 PMCID: PMC9672551 DOI: 10.1007/s00521-022-07998-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022]
Abstract
This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework.
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8
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Albahri AS, Hamid RA, Zaidan AA, Albahri OS. Early automated prediction model for the diagnosis and detection of children with autism spectrum disorders based on effective sociodemographic and family characteristic features. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07822-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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9
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Telehealth and Transformation of Nursing Care in Saudi Arabia: A Systematic Review. Int J Telemed Appl 2022; 2022:8426095. [PMID: 36249324 PMCID: PMC9553846 DOI: 10.1155/2022/8426095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction Technological advancements have transformed nursing care, quality, and education across the globe. In the Kingdom of Saudi Arabia (KSA), the inventions and adoption of mobile technologies such as an e-health application (app) called SEHA continue to revolutionize the healthcare system in the country. Purpose The present systematic review is aimed at examining the technological impact on nursing in Saudi Arabia. The study provides a comprehensive analysis of telehealth and its role in nursing quality, nursing practice, and education. Methods The present study adopted a literature review methodology by deriving data from journal articles from different databases, for example, Web Science, Google Scholar, CINAHL, MEDLINE, and PubMed databases. Inclusive years for the search ranged from 2016 to 2022. A total of eight articles were found dovetailing to meet the research objectives and answer research questions. Result After appraising and analyzing the research, the present review found that (Abolfotouh et al., 2019) telehealth in nursing is loosely researched; (Ahmed et al., 2021) telehealth impacts nursing practice and quality by fostering nurse-patient communication promoting positive outcomes, seamless nursing care, and positive experiences; and (Albahri et al., 2021) telehealth and telemedicine is a central tenet of contemporary nursing education and practice. Conclusion From these findings, this analysis informed three key recommendations: the need to integrate telehealth into the nursing curriculum, telehealth training, and reskilling among healthcare workers (HCWs) in KSA and further primary studies focusing predominantly on telenursing. Overall, telehealth remains a fundamental transformation of nursing practice that forms a central ideology in the contemporary nursing process.
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10
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Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review. Int J Telemed Appl 2022; 2022:3551528. [PMID: 35814280 PMCID: PMC9270139 DOI: 10.1155/2022/3551528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/31/2022] [Accepted: 06/13/2022] [Indexed: 11/18/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” (
). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of “diagnosis ASD based on medical and family characteristic features” (
). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.
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11
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Joudar SS, Albahri AS, Hamid RA. Triage and priority-based healthcare diagnosis using artificial intelligence for autism spectrum disorder and gene contribution: A systematic review. Comput Biol Med 2022; 146:105553. [PMID: 35561591 DOI: 10.1016/j.compbiomed.2022.105553] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 04/03/2022] [Accepted: 04/20/2022] [Indexed: 11/03/2022]
Abstract
The exact nature, harmful effects and aetiology of autism spectrum disorder (ASD) have caused widespread confusion. Artificial intelligence (AI) science helps solve challenging diagnostic problems in the medical field through extensive experiments. Disease severity is closely related to triage decisions and prioritisation contexts in medicine because both have been widely used to diagnose various diseases via AI, machine learning and automated decision-making techniques. Recently, taking advantage of high-performance AI algorithms has achieved accessible success in diagnosing and predicting risks from clinical and biological data. In contrast, less progress has been made with ASD because of obscure reasons. According to academic literature, ASD diagnosis works from a specific perspective, and much of the confusion arises from the fact that how AI techniques are currently integrated with the diagnosis of ASD concerning the triage and priority strategies and gene contributions. To this end, this study sought to describe a systematic review of the literature to assess the respective AI methods using the available datasets, highlight the tools and strategies used for diagnosing ASD and investigate how AI trends contribute in distinguishing triage and priority for ASD and gene contributions. Accordingly, this study checked the Science Direct, IEEE Xplore Digital Library, Web of Science (WoS), PubMed, and Scopus databases. A set of 363 articles from 2017 to 2022 is collected to reveal a clear picture and a better understanding of all the academic literature through a final set of 18 articles. The retrieved articles were filtered according to the defined inclusion and exclusion criteria and classified into three categories. The first category includes 'Triage patients based on diagnosis methods' which accounts for 16.66% (n = 3/18). The second category includes 'Prioritisation for Risky Genes' which accounts for 66.6% (n = 12/18) and is classified into two subcategories: 'Mutations observation based', 'Biomarkers and toxic chemical observations'. The third category includes 'E-triage using telehealth' which accounts for 16.66% (n = 3/18). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations and challenges of ASD research that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and discusses the open issues that help perform and improve the recommended solution of ASD research direction. In addition, this study critically reviews the literature and attempts to address the current research gaps in knowledge and highlights weaknesses that require further research. Finally, a new developed methodology has been suggested as future work for triaging and prioritising ASD patients according to their severity levels by using decision-making techniques.
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Affiliation(s)
- Shahad Sabbar Joudar
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq; University of Technology, Baghdad, Iraq
| | - A S Albahri
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq.
| | - Rula A Hamid
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq; College of Business Informatics, University of Information Technology and Communications (UOITC), Baghdad, Iraq
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12
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Alsalem MA, Mohammed R, Albahri OS, Zaidan AA, Alamoodi AH, Dawood K, Alnoor A, Albahri AS, Zaidan BB, Aickelin U, Alsattar H, Alazab M, Jumaah F. Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature. INT J INTELL SYST 2022; 37:3514-3624. [PMID: 38607836 PMCID: PMC8653072 DOI: 10.1002/int.22699] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 09/08/2021] [Accepted: 09/16/2021] [Indexed: 12/17/2022]
Abstract
Considering the coronavirus disease 2019 (COVID-19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision-making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID-19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID-19 by presenting a systematic literature review of the state-of-the-art COVID-19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.
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Affiliation(s)
- Mohammed Assim Alsalem
- Department of Computing, Faculty of Arts, Computing and Creative IndustryUniversiti Pendidikan Sultan IdrisTanjung MalimMalaysia
| | - Rawia Mohammed
- Faculty of Computing and Innovative TechnologyGeomatika University CollegeKuala LumpurMalaysia
| | - Osamah Shihab Albahri
- Department of Computing, Faculty of Arts, Computing and Creative IndustryUniversiti Pendidikan Sultan IdrisTanjung MalimMalaysia
| | - Aws Alaa Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative IndustryUniversiti Pendidikan Sultan IdrisTanjung MalimMalaysia
| | - Abdullah Hussein Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative IndustryUniversiti Pendidikan Sultan IdrisTanjung MalimMalaysia
| | - Kareem Dawood
- Computer Science DepartmentKomar University of Science and Technology (KUST)SulaymaniyahIraq
| | - Alhamzah Alnoor
- School of ManagementUniversiti Sains MalaysiaPulau PinangMalaysia
| | - Ahmed Shihab Albahri
- Informatics Institute for Postgraduate Studies (IIPS)Iraqi Commission for Computers and Informatics (ICCI)BaghdadIraq
| | - Bilal Bahaa Zaidan
- Future Technology Research CenterNational Yunlin University of Science and TechnologyDouliouTaiwan R.O.C.
| | - Uwe Aickelin
- School of Computing and Information SystemsThe University of MelbourneAustralia
| | - Hassan Alsattar
- Department of Computing, Faculty of Arts, Computing and Creative IndustryUniversiti Pendidikan Sultan IdrisTanjung MalimMalaysia
| | - Mamoun Alazab
- College of Engineering, IT and EnvironmentCharles Darwin UniversityCasuarinaNorthern TerritoryAustralia
| | - Fawaz Jumaah
- Department of Advanced Applications and Embedded SystemsIntel CorporationPulau PinangMalaysia
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Kobeissi MM, Ruppert SD. Remote patient triage: Shifting toward safer telehealth practice. J Am Assoc Nurse Pract 2022; 34:444-451. [PMID: 34519672 PMCID: PMC8893128 DOI: 10.1097/jxx.0000000000000655] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 08/03/2021] [Accepted: 08/13/2021] [Indexed: 11/26/2022]
Abstract
ABSTRACT Telehealth is a tool used to diagnose and treat patients at a distance. Telehealth quickly became essential during the COVID-19 pandemic as a result of stay-at-home orders. Regulatory waivers encouraged the use of telehealth as an alternative to the in-person encounter to limit the spread of disease. The pandemic incited a rapid growth in telehealth, and new legislation, new technologies, and providers new to virtual care changed the delivery of traditional telehealth. Postpandemic planning is necessary to support the safe integration of telehealth in the health care system. The purpose of this article is to discuss the current issues affecting telehealth and offer recommendations for safer virtual care. Critical considerations, beginning with an assessment of remote patient acuity, are needed to ensure the standard of care for telehealth is equivalent to the in-person setting. A triage protocol to screen patients seeking virtual services is required to prevent underestimation of severity of illness, sort patients to place of service, and determine if a need exists to escalate to an in-person evaluation or higher level of care. A standard approach to triage may minimize the risks to patient safety and support the appropriate use of telehealth technologies.
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Affiliation(s)
- Mahrokh M. Kobeissi
- Department of Graduate Studies, Cizik School of Nursing at UTHealth Houston, Houston, Texas
| | - Susan D. Ruppert
- Department of Graduate Studies, Cizik School of Nursing at UTHealth Houston, Houston, Texas
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Albahri AS, Albahri OS, Zaidan AA, Alnoor A, Alsattar HA, Mohammed R, Alamoodi AH, Zaidan BB, Aickelin U, Alazab M, Garfan S, Ahmaro IYY, Ahmed MA. Integration of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score methods under a q-rung orthopair environment: A distribution case study of COVID-19 vaccine doses. COMPUTER STANDARDS & INTERFACES 2022; 80:103572. [PMID: 34456503 PMCID: PMC8386109 DOI: 10.1016/j.csi.2021.103572] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 08/14/2021] [Accepted: 08/22/2021] [Indexed: 05/26/2023]
Abstract
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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Affiliation(s)
- A S Albahri
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - O S Albahri
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - A A Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - Alhamzah Alnoor
- School of Management, Universiti Sains Malaysia, Pulau Pinang, Malaysia
| | - H A Alsattar
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - Rawia Mohammed
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - A H Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - B B Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - Uwe Aickelin
- School of Computing and Information Systems, University of Melbourne, 700 Swanston Street, Victoria 3010 Australia
| | - Mamoun Alazab
- College of Engineering, IT and Environment, Charles Darwin University, NT, Australia
| | - Salem Garfan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - Ibraheem Y Y Ahmaro
- Computer Science Department, College of Information Technology, Hebron University, Hebron, Palestine
| | - M A Ahmed
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
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15
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A new extension of FDOSM based on Pythagorean fuzzy environment for evaluating and benchmarking sign language recognition systems. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06683-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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16
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Alsalem MA, Albahri OS, Zaidan AA, Al-Obaidi JR, Alnoor A, Alamoodi AH, Albahri AS, Zaidan BB, Jumaah FM. Rescuing emergency cases of COVID-19 patients: An intelligent real-time MSC transfusion framework based on multicriteria decision-making methods. APPL INTELL 2022; 52:9676-9700. [PMID: 35035091 PMCID: PMC8741536 DOI: 10.1007/s10489-021-02813-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/23/2021] [Indexed: 12/16/2022]
Abstract
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.
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Hybrid artificial neural network and structural equation modelling techniques: a survey. COMPLEX INTELL SYST 2022; 8:1781-1801. [PMID: 34777975 PMCID: PMC8402975 DOI: 10.1007/s40747-021-00503-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/12/2021] [Indexed: 02/07/2023]
Abstract
Topical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM-ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM-ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM-ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore ® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM-ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.
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Alsalem MA, Alsattar HA, Albahri AS, Mohammed RT, Albahri OS, Zaidan AA, Alnoor A, Alamoodi AH, Qahtan S, Zaidan BB, Aickelin U, Alazab M, Jumaah FM. Based on T-spherical fuzzy environment: A combination of FWZIC and FDOSM for prioritising COVID-19 vaccine dose recipients. J Infect Public Health 2021; 14:1513-1559. [PMID: 34538731 PMCID: PMC8388152 DOI: 10.1016/j.jiph.2021.08.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 08/14/2021] [Accepted: 08/21/2021] [Indexed: 01/07/2023] Open
Abstract
The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted decision matrix used in the COVID-19 vaccine distribution. The second phase presented the sequential formulation steps of T-SFWZIC used for weighting the distribution criteria followed by T-SFDOSM utilised for prioritising the vaccine recipients. Results revealed the following: (1) T-SFWZIC effectively weighted the vaccine distribution criteria based on several parameters including T = 2, T = 4, T = 6, T = 8, and T = 10. Amongst all parameters, the age criterion received the highest weight, whereas the geographic locations severity criterion has the lowest weight. (2) According to the T parameters, a considerable variance has occurred on the vaccine recipient orders, indicating that the existence of T values affected the vaccine distribution. (3) In the individual context of T-SFDOSM, no unique prioritisation was observed based on the obtained opinions of each expert. (4) The group context of T-SFDOSM used in the prioritisation of vaccine recipients was considered the final distribution result as it unified the differences found in an individual context. The evaluation was performed based on systematic ranking assessment and sensitivity analysis. This evaluation showed that the prioritisation results based on each T parameter were subject to a systematic ranking that is supported by high correlation results over all discussed scenarios of changing criteria weights values.
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Affiliation(s)
- M A Alsalem
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - H A Alsattar
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - A S Albahri
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - R T Mohammed
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia; Faculty of Computing and Innovative Technology, Geomatika University College, Kuala Lumpur, Malaysia
| | - O S Albahri
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - A A Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia.
| | - Alhamzah Alnoor
- School of Management, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia
| | - A H Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - Sarah Qahtan
- Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - B B Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia; Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
| | - Uwe Aickelin
- School of Computing and Information Systems, University of Melbourne, 700 Swanston Street, Victoria 3010, Australia
| | - Mamoun Alazab
- College of Engineering, IT and Environment, Charles Darwin University, NT, Australia
| | - F M Jumaah
- Department of Advanced Applications and Embedded Systems, Intel Corporation, Plot 6 Bayan Lepas Technoplex, 11900 Pulau Pinang, Malaysia
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19
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Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients. J Adv Res 2021; 37:147-168. [PMID: 35475277 PMCID: PMC8378994 DOI: 10.1016/j.jare.2021.08.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 08/09/2021] [Accepted: 08/12/2021] [Indexed: 12/14/2022] Open
Abstract
Introduction The vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues. Objectives This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods. Methods The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM. Results (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values. Conclusion The findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.
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Albahri AS, Zaidan AA, Albahri OS, Zaidan BB, Alamoodi AH, Shareef AH, Alwan JK, Hamid RA, Aljbory MT, Jasim AN, Baqer MJ, Mohammed KI. Development of IoT-based mhealth framework for various cases of heart disease patients. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00579-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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21
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Krishnan E, Mohammed R, Alnoor A, Albahri OS, Zaidan AA, Alsattar H, Albahri AS, Zaidan BB, Kou G, Hamid RA, Alamoodi AH, Alazab M. Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications. INT J INTELL SYST 2021. [DOI: 10.1002/int.22489] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Elaiyaraja Krishnan
- Department of Computing, Faculty of Arts, Computing and Creative Industry Sultan Idris Education University Tanjung Malim Malaysia
| | - Rawia Mohammed
- Department of Computing, Faculty of Arts, Computing and Creative Industry Sultan Idris Education University Tanjung Malim Malaysia
| | - Alhamzah Alnoor
- School of Management Universiti Sains Malaysia Pulau Pinang Malaysia
| | - Osamah Shihab Albahri
- Department of Computing, Faculty of Arts, Computing and Creative Industry Sultan Idris Education University Tanjung Malim Malaysia
| | - Aws Alaa Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry Sultan Idris Education University Tanjung Malim Malaysia
| | - Hassan Alsattar
- Department of Computing, Faculty of Arts, Computing and Creative Industry Sultan Idris Education University Tanjung Malim Malaysia
| | - Ahmed Shihab Albahri
- Department of Computing, Faculty of Arts, Computing and Creative Industry Sultan Idris Education University Tanjung Malim Malaysia
- Informatics Institute for Postgraduate Studies (IIPS) Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq
| | - Bilal Bahaa Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry Sultan Idris Education University Tanjung Malim Malaysia
- Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan, ROC
| | - Gang Kou
- School of Business Administration Southwestern University of Finance and Economics Chengdu China
| | - Rula A. Hamid
- College of Business Informatics University of Information Technology and Communications (UOITC) Baghdad Iraq
| | - Abdullah Hussein Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative Industry Sultan Idris Education University Tanjung Malim Malaysia
| | - Mamoun Alazab
- College of Engineering, IT and Environment Charles Darwin University NT Australia
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22
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Mohsin AH, Zaidan AA, Zaidan BB, Mohammed KI, Albahri OS, Albahri AS, Alsalem MA. PSO-Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:14137-14161. [PMID: 33519293 PMCID: PMC7821848 DOI: 10.1007/s11042-020-10284-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 09/17/2020] [Accepted: 12/22/2020] [Indexed: 05/02/2023]
Abstract
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.
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Affiliation(s)
- A. H. Mohsin
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak Malaysia
- Republic of Iraq-Presidency of Ministries - Establishment of Martyrs, Baghdad, Iraq
| | - A. A. Zaidan
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak Malaysia
| | - B. B. Zaidan
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak Malaysia
| | - K. I. Mohammed
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak Malaysia
| | - O. S. Albahri
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak Malaysia
| | - A. S. Albahri
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak Malaysia
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - M. A. Alsalem
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak Malaysia
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23
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Mohammed TJ, Albahri AS, Zaidan AA, Albahri OS, Al-Obaidi JR, Zaidan BB, Larbani M, Mohammed RT, Hadi SM. Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component. APPL INTELL 2021; 51:2956-2987. [PMID: 34764579 PMCID: PMC7820530 DOI: 10.1007/s10489-020-02169-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2020] [Indexed: 01/31/2023]
Abstract
As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CP-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified 'as a proof of concept'. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five 'serological/protein biomarker' criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.
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Affiliation(s)
- Thura J. Mohammed
- grid.444506.70000 0000 9272 6490Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia ,Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - A. S. Albahri
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - A. A. Zaidan
- grid.444506.70000 0000 9272 6490Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia
| | - O. S. Albahri
- grid.444506.70000 0000 9272 6490Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia
| | - Jameel R. Al-Obaidi
- grid.444506.70000 0000 9272 6490Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak Malaysia
| | - B. B. Zaidan
- grid.444506.70000 0000 9272 6490Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia
| | - Moussa Larbani
- grid.34428.390000 0004 1936 893XSchool of Mathematics and Statistics, Carleton University, Ottawa, ON Canada
| | - R. T. Mohammed
- grid.11142.370000 0001 2231 800XFaculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - Suha M. Hadi
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
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24
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Lewinski AA, Rushton S, Van Voorhees E, Boggan JC, Whited JD, Shoup JP, Tabriz AA, Adam S, Fulton J, Gordon AM, Ear B, Williams JW, Goldstein KM, Van Noord MG, Gierisch JM. Implementing remote triage in large health systems: A qualitative evidence synthesis. Res Nurs Health 2020; 44:138-154. [PMID: 33319411 DOI: 10.1002/nur.22093] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/20/2020] [Accepted: 11/28/2020] [Indexed: 01/06/2023]
Abstract
Remote triage (RT) allows interprofessional teams (e.g., nurses and physicians) to assess patients and make clinical decisions remotely. RT use has developed widespread interest due to the COVID-19 pandemic, and has future potential to address the needs of a rapidly aging population, improve access to care, facilitate interprofessional team care, and ensure appropriate use of resources. However, despite rapid and increasing interest in implementation of RT, there is little research concerning practices for successful implementation. We conducted a systematic review and qualitative evidence synthesis of practices that impact the implementation of RT for adults seeking clinical care advice. We searched MEDLINE®, EMBASE, and CINAHL from inception through July 2018. We included 32 studies in this review. Our review identified four themes impacting the implementation of RT: characteristics of staff who use RT, influence of RT on staff, considerations in selecting RT tools, and environmental and contextual factors impacting RT. The findings of our systemic review underscore the need for a careful consideration of (a) organizational and stakeholder buy-in before launch, (b) physical and psychological workplace environment, (c) staff training and ongoing support, and (d) optimal metrics to assess the effectiveness and efficiency of implementation. Our findings indicate that preimplementation planning, as well as evaluating RT by collecting data during and after implementation, is essential to ensuring successful implementation and continued adoption of RT in a health care system.
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Affiliation(s)
- Allison A Lewinski
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina, USA.,School of Nursing, Duke University, Durham, North Carolina, USA
| | - Sharron Rushton
- School of Nursing, Duke University, Durham, North Carolina, USA
| | - Elizabeth Van Voorhees
- Durham Veterans Affairs Health Care System, Durham, North Carolina, USA.,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Joel C Boggan
- Durham Veterans Affairs Health Care System, Durham, North Carolina, USA.,Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - John D Whited
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina, USA.,Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | | | - Amir A Tabriz
- Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy University of North Carolina, Chapel Hill, North Carolina, USA
| | - Soheir Adam
- Division of Hematology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jessica Fulton
- Durham Veterans Affairs Health Care System, Durham, North Carolina, USA.,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Adelaide M Gordon
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina, USA
| | - Belinda Ear
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina, USA
| | - John W Williams
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina, USA.,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA.,Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Karen M Goldstein
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina, USA.,Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Megan G Van Noord
- Carlson Health Sciences Library, University of California, Davis, California, USA
| | - Jennifer M Gierisch
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina, USA.,Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
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25
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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. 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. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105617. [PMID: 32593060 PMCID: PMC7305916 DOI: 10.1016/j.cmpb.2020.105617] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 06/16/2020] [Indexed: 05/04/2023]
Abstract
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.
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Affiliation(s)
- O S Albahri
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia
| | - Jameel R Al-Obaidi
- Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak 35900, Malaysia
| | - A A Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia.
| | - A S Albahri
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - B B Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia
| | - Mahmood M Salih
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit 34001, Iraq
| | - Abdulhadi Qays
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia
| | - K A Dawood
- Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - R T Mohammed
- Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - Karrar Hameed Abdulkareem
- Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia
| | - A M Aleesa
- Faculty of Electronic and Electrical Engineering, Universiti Tun Hussein Onn, Batu Pahat, Johor 86400, Malaysia
| | - A H Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia
| | - M A Chyad
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia
| | - Che Zalina Zulkifli
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan, Tanjung Malim 35900, Malaysia
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Bhaskar S, Bradley S, Chattu VK, Adisesh A, Nurtazina A, Kyrykbayeva S, Sakhamuri S, Moguilner S, Pandya S, Schroeder S, Banach M, Ray D. Telemedicine as the New Outpatient Clinic Gone Digital: Position Paper From the Pandemic Health System REsilience PROGRAM (REPROGRAM) International Consortium (Part 2). Front Public Health 2020; 8:410. [PMID: 33014958 PMCID: PMC7505101 DOI: 10.3389/fpubh.2020.00410] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/10/2020] [Indexed: 12/11/2022] Open
Abstract
Technology has acted as a great enabler of patient continuity through remote consultation, ongoing monitoring, and patient education using telephone and videoconferencing in the coronavirus disease 2019 (COVID-19) era. The devastating impact of COVID-19 is bound to prevail beyond its current reign. The vulnerable sections of our community, including the elderly, those from lower socioeconomic backgrounds, those with multiple comorbidities, and immunocompromised patients, endure a relatively higher burden of a pandemic such as COVID-19. The rapid adoption of different technologies across countries, driven by the need to provide continued medical care in the era of social distancing, has catalyzed the penetration of telemedicine. Limiting the exposure of patients, healthcare workers, and systems is critical in controlling the viral spread. Telemedicine offers an opportunity to improve health systems delivery, access, and efficiency. This article critically examines the current telemedicine landscape and challenges in its adoption, toward remote/tele-delivery of care, across various medical specialties. The current consortium provides a roadmap and/or framework, along with recommendations, for telemedicine uptake and implementation in clinical practice during and beyond COVID-19.
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Affiliation(s)
- Sonu Bhaskar
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Department of Neurology, Liverpool Hospital and South Western Sydney Local Health District, Sydney, NSW, Australia.,Neurovascular Imaging Laboratory & NSW Brain Clot Bank, Ingham Institute for Applied Medical Research, Sydney, NSW, Australia.,South Western Sydney Clinical School, The University of New South Wales, UNSW Medicine, Sydney, NSW, Australia
| | - Sian Bradley
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,The University of New South Wales (UNSW) Medicine Sydney, South West Sydney Clinical School, Sydney, NSW, Australia
| | - Vijay Kumar Chattu
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Department of Medicine, University of Toronto, Toronto, ON, Canada.,St. Michael's Hospital, Toronto, ON, Canada
| | - Anil Adisesh
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Department of Medicine, University of Toronto, Toronto, ON, Canada.,St. Michael's Hospital, Toronto, ON, Canada
| | - Alma Nurtazina
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Department of Epidemiology and Biostatistics, Semey Medical University, Semey, Kazakhstan
| | - Saltanat Kyrykbayeva
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Department of Epidemiology and Biostatistics, Semey Medical University, Semey, Kazakhstan
| | - Sateesh Sakhamuri
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Department of Clinical Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
| | - Sebastian Moguilner
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Shawna Pandya
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Alberta Health Services and Project PoSSUM, University of Alberta, Edmonton, AB, Canada
| | - Starr Schroeder
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Penn Medicine Lancaster General Hospital and Project PoSSUM, Lancaster, PA, United States
| | - Maciej Banach
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Polish Mother's Memorial Hospital Research Institute (PMMHRI), Łódz, Poland.,Cardiovascular Research Centre, University of Zielona Gora, Zielona Gora, Poland.,Department of Hypertension, Medical University of Lodz, Łódz, Poland
| | - Daniel Ray
- Pandemic Health System REsilience PROGRAM (REPROGRAM) Consortium, REPROGRAM Telemedicine Sub-committee, Sydney, NSW, Australia.,Farr Institute of Health Informatics, University College London (UCL) & NHS Foundation Trust, Birmingham, United Kingdom
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27
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A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05020-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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