1
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Rao GM, Ramesh D, Sharma V, Sinha A, Hassan MM, Gandomi AH. AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach. Sci Rep 2024; 14:7833. [PMID: 38570560 PMCID: PMC10991318 DOI: 10.1038/s41598-024-56931-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 03/12/2024] [Indexed: 04/05/2024] Open
Abstract
Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.
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Affiliation(s)
- G Madhukar Rao
- Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, India
| | - Dharavath Ramesh
- Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India
- Department of Computer Science, University of Economics and Human Sciences, Warsaw, Poland
| | - Vandana Sharma
- Computer Science Department, Christ University, Delhi NCR Campus, Ghaziabad, Delhi NCR, India
| | - Anurag Sinha
- Department of Computer Science, ICFAI Tech School, ICFAI University, Ranchi, Jharkhand, India
| | - Md Mehedi Hassan
- Computer Science and Engineering, Discipline Khulna University, Khulna, 9208, Bangladesh
| | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
- University Research and Innovation Center (EKIK), Óbuda University, 1034, Budapest, Hungary.
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2
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Kholaif MMNHK, Xiao M. Is it an opportunity? COVID-19's effect on the green supply chains, and perceived service's quality (SERVQUAL): the moderate effect of big data analytics in the healthcare sector. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:14365-14384. [PMID: 36152097 PMCID: PMC9510201 DOI: 10.1007/s11356-022-23173-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
This study examines the relationship between uncertainty-fear toward COVID-19, green supply chain management (GSCM), and perceived service quality based on the five dimensions service quality model (SERVQUAL). It also tests the moderating effect of big data analytics (BDA) capabilities. Based on a sample of 300 healthcare managers and customers, we used partial least squares structural equation modeling to analyze the data and test our hypotheses. The empirical results show that the uncertainty-fear toward COVID-19 positively affects GSCM. Also, BDA moderates the relationship between uncertainty-fear toward COVID-19 and GSCM. GSCM positively impacts service quality (empathy, responsiveness, and assurance) but not reliability or tangible items. In addition, GSCM significantly mediates the relationship between uncertainty-fear toward COVID-19 and services' empathy, responsiveness, and assurance. However, it has an insignificant mediation effect regarding reliability and tangible-item dimensions.
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Affiliation(s)
| | - Ming Xiao
- School of Economics and Management, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083 China
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3
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Oshni Alvandi A, Burstein F, Bain C. A digital health ecosystem ontology from the perspective of Australian consumers: a mixed-method literature analysis. Inform Health Soc Care 2023; 48:13-29. [PMID: 35298327 DOI: 10.1080/17538157.2022.2049273] [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: 11/05/2022]
Abstract
This study presents an ontology that scopes the digital health ecosystem from a consumer-centered perspective. We used a mixed-method analysis on a set of papers collected for a comprehensive review to identify common themes, components, and patterns that repeatedly emerge within Australian-based digital health studies. Three major and four child themes were identified as the foundational aspects of the proposed ontology. The child themes have more precise concept definitions, inherited and distinguishing attributes. Out of 179 recognized concepts, 33 were related to the Healthcare theme; 23 concepts formed a cluster of employed devices under the Technology theme; 40 concepts were associated with Use and Usability factors. 60 other concepts formed the cluster of the consumer-user theme. The theme of Digital Health was seen as being connected to 2 independent clusters. The main cluster embodied 21 extracted concepts, semantically related to "data, information, and knowledge," whilst the second cluster embodied concepts related to "healthcare." Different stakeholders can utilize this ontology to define their landscape of digitally enabled healthcare. The novelty of this work resides in capturing a consumer-centered perspective and the method we used in deriving the ontology - formalizing the results of a systematic review based on data-driven analysis methods.
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Affiliation(s)
- Abraham Oshni Alvandi
- Digital Health Theme, Department of Human-Centered Computing, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Frada Burstein
- Digital Health Theme, Department of Human-Centered Computing, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Chris Bain
- Digital Health Theme, Department of Human-Centered Computing, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
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4
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Gomes MAS, Kovaleski JL, Pagani RN, da Silva VL, Pasquini TCDS. Transforming healthcare with big data analytics: technologies, techniques and prospects. J Med Eng Technol 2023; 47:1-11. [PMID: 35852400 DOI: 10.1080/03091902.2022.2096133] [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: 01/11/2023]
Abstract
In different studies in the field of healthcare, big data analytics technology has been shown to be effective in observing the behaviour of data, of which analysed to allow the discovery of relevant insights for strategy and decision making. The objective of this study is to present the results of a systematic review of the literature on big data analytics in healthcare, focussing in technologies, main areas and purposes of adoption. To reach its objective, the study conducts an exploratory research, through a systematic review of the literature, using the Methodi Ordinatio protocol supported by content analysis. The results reveal that the use of tools implies work performance at the clinical and managerial level, improving the cost-benefit ratio and reducing the time factor in the practice of the workforce in health services. Thus, this study hopes to contribute to the technological advancement of computational intelligence applied to healthcare.
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Affiliation(s)
- Myller Augusto Santos Gomes
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - João Luiz Kovaleski
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - Regina Negri Pagani
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - Vander Luiz da Silva
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
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5
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Khan S, Khan HU, Nazir S. Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing. Sci Rep 2022; 12:22377. [PMID: 36572709 PMCID: PMC9792582 DOI: 10.1038/s41598-022-26090-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/09/2022] [Indexed: 12/27/2022] Open
Abstract
Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.
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Affiliation(s)
- Sulaiman Khan
- Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
| | - Habib Ullah Khan
- Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
| | - Shah Nazir
- Department of Computer Science, University of Swabi, Swabi, Pakistan
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6
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Badakhshan P, Wurm B, Grisold T, Geyer-Klingeberg J, Mendling J, vom Brocke J. Creating business value with process mining. JOURNAL OF STRATEGIC INFORMATION SYSTEMS 2022. [DOI: 10.1016/j.jsis.2022.101745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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7
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Ronaghi MH. Toward a model for assessing smart hospital readiness within the Industry 4.0 paradigm. JOURNAL OF SCIENCE AND TECHNOLOGY POLICY MANAGEMENT 2022. [DOI: 10.1108/jstpm-09-2021-0130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The fourth industrial revolution and digital transformation have caused paradigm changes in the procedures of goods production and services through disruptive technologies, and they have formed new methods for business models. Health and medicine fields have been under the effect of these technology advancements. The concept of smart hospital is formed according to these technological transformations. The aim of this research, other than explanation of smart hospital components, is to present a model for evaluating a hospital readiness for becoming a smart hospital.
Design/methodology/approach
This research is an applied one, and has been carried out in three phases and according to design science research. Based on the previous studies, in the first phase, the components and technologies effecting a smart hospital are recognized. In the second phase, the extracted components are prioritized using type-2 fuzzy analytic hierarchical process based on the opinion of experts; later, the readiness model is designed. In the third phase, the presented model would be tested in a hospital.
Findings
The research results showed that the technologies of internet of things, robotics, artificial intelligence, radio-frequency identification as well as augmented and virtual reality had the most prominence in a smart hospital.
Originality/value
The innovation and originality of the forthcoming research is to explain the concept of smart hospital, to rank its components and to provide a model for evaluating the readiness of smart hospital. Contribution of this research in terms of theory explains the concept of smart hospital and in terms of application presents a model for assessing the readiness of smart hospitals.
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8
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Xu D, Xu Z. Bibliometric analysis of decision-making in healthcare management from 1998 to 2021. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2134641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Duo Xu
- Business School, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Zeshui Xu
- Business School, Sichuan University, Chengdu, People’s Republic of China
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9
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Saha E, Rathore P. Discovering hidden patterns among medicines prescribed to patients using Association Rule Mining Technique. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2099335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Esha Saha
- Institute of Management Technology Hyderabad, Hyderabad, India
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10
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Financial Data Analysis and Application Based on Big Data Mining Technology. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6711470. [PMID: 35789614 PMCID: PMC9250444 DOI: 10.1155/2022/6711470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022]
Abstract
We provide a brief overview of the connotation and characteristics of data mining technology in the era of big data, analyze the feasibility of data mining technology in business management from the economic and technical perspectives, and propose specific application suggestions according to the content and requirements of business management. This paper describes in detail the principles and steps of using the weighted plain Bayesian algorithm and the decision tree algorithm to analyze students' performance; firstly, we need to obtain the plain Bayesian analysis model of college students' learning literacy in physical education and the C4.5 graduation literacy analysis model, and then use certain rules to combine the weighted plain Bayesian algorithm and the decision tree algorithm to obtain the WNB-C4.5 college students' learning literacy analysis model. In addition, in the prediction of financial risks, the classification scheme can be used in the judgment of violation of regulations, but the most used classification scheme is the decision tree. Experiments show that the effectiveness of this scheme in data mining for financial companies is increased by 2% compared to the benchmark method.
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11
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Galetsi P, Katsaliaki K, Kumar S. The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19. Soc Sci Med 2022; 301:114973. [PMID: 35452893 PMCID: PMC9001170 DOI: 10.1016/j.socscimed.2022.114973] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 02/21/2022] [Accepted: 04/08/2022] [Indexed: 12/23/2022]
Abstract
With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.
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Affiliation(s)
- Panagiota Galetsi
- School of Humanities, Social Sciences and Economics, International Hellenic University, 14th Km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece
| | - Korina Katsaliaki
- School of Humanities, Social Sciences and Economics, International Hellenic University, 14th Km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece
| | - Sameer Kumar
- Opus College of Business, University of St. Thomas Minneapolis Campus 1000 LaSalle Ave, Schulze Hall 333, Minneapolis, MN, 55403, USA.
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12
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Tang KY, Hsiao CH, Hwang GJ. A scholarly network of AI research with an information science focus: Global North and Global South perspectives. PLoS One 2022; 17:e0266565. [PMID: 35427381 PMCID: PMC9012391 DOI: 10.1371/journal.pone.0266565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 03/22/2022] [Indexed: 11/19/2022] Open
Abstract
This paper primarily aims to provide a citation-based method for exploring the scholarly network of artificial intelligence (AI)-related research in the information science (IS) domain, especially from Global North (GN) and Global South (GS) perspectives. Three research objectives were addressed, namely (1) the publication patterns in the field, (2) the most influential articles and researched keywords in the field, and (3) the visualization of the scholarly network between GN and GS researchers between the years 2010 and 2020. On the basis of the PRISMA statement, longitudinal research data were retrieved from the Web of Science and analyzed. Thirty-two AI-related keywords were used to retrieve relevant quality articles. Finally, 149 articles accompanying the follow-up 8838 citing articles were identified as eligible sources. A co-citation network analysis was adopted to scientifically visualize the intellectual structure of AI research in GN and GS networks. The results revealed that the United States, Australia, and the United Kingdom are the most productive GN countries; by contrast, China and India are the most productive GS countries. Next, the 10 most frequently co-cited AI research articles in the IS domain were identified. Third, the scholarly networks of AI research in the GN and GS areas were visualized. Between 2010 and 2015, GN researchers in the IS domain focused on applied research involving intelligent systems (e.g., decision support systems); between 2016 and 2020, GS researchers focused on big data applications (e.g., geospatial big data research). Both GN and GS researchers focused on technology adoption research (e.g., AI-related products and services) throughout the investigated period. Overall, this paper reveals the intellectual structure of the scholarly network on AI research and several applications in the IS literature. The findings provide research-based evidence for expanding global AI research.
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Affiliation(s)
- Kai-Yu Tang
- Department of International Business, Ming Chuan University, Taipei, Taiwan
- * E-mail:
| | | | - Gwo-Jen Hwang
- Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei, Taiwan
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13
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Ali I, Kannan D. Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review. ANNALS OF OPERATIONS RESEARCH 2022; 315:29-55. [PMID: 35382453 PMCID: PMC8972768 DOI: 10.1007/s10479-022-04596-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/14/2022] [Indexed: 06/14/2023]
Abstract
The literature on healthcare operations and supply chain management has seen unprecedented growth over the past two decades. This paper seeks to advance the body of knowledge on this topic by utilising a topic modelling-based literature review to identify the core topics, examine their dynamic changes, and identify opportunities for further research in the area. Based on an analysis of 571 articles published until 25 January 2022, we identify numerous popular topics of research in the area, including patient waiting time, COVID-19 pandemic, Industry 4.0 technologies, sustainability, risk and resilience, climate change, circular economy, humanitarian logistics, behavioural operations, service-ecosystem, and knowledge management. We reviewed current literature around each topic and offered insights into what aspects of each topic have been studied and what are the recent developments and opportunities for more impactful future research. Doing so, this review help advance the contemporary scholarship on healthcare operations and supply chain management and offers resonant insights for researchers, research students, journal editors, and policymakers in the field.
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Affiliation(s)
- Imran Ali
- School of Business and Law, CQ University, Rockhampton North Campus, Sydney, Australia
| | - Devika Kannan
- SDU- Center for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, Campusvej 55, Odense, Denmark
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14
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Kumar P, Chakraborty S. Green service production and environmental performance in healthcare emergencies: role of big-data management and green HRM practices. INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT 2022. [DOI: 10.1108/ijlm-02-2021-0075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
This study aims to examine the impact of big data management on green service production (GSP) and environmental performance (ENPr) while considering green HRM practices (GHRM) in healthcare emergencies.
Design/methodology/approach
The authors collected primary data from major healthcare organizations in India by surveying healthcare professionals. The data analysis through structural equation modelling (PLS-SEM) reveals several significant relationships to extricate the underlying dynamics.
Findings
Grounded in the theories of service production and natural resource-based view (NRBV), this study conceptualizes GSP with its three dimensions of green procurement (GP), green service design (GSD) and green service practices (GSPr). The study conducted in India's healthcare sector with a sample size limited to healthcare professionals serving in COVID-19 identifies the positive and significant impact of big data management on GSP and ENPr that organizations seek to deploy in such emergencies. The findings of the study explain the moderating effects of GHRM on GSP-ENPr relationships.
Research limitations/implications
The study was conducted in the healthcare sector in India, and its sample size was limited to healthcare professionals serving in COVID-19. The practical ramifications for healthcare administrators and policymakers are suggested, and future avenues of research are discussed.
Originality/value
This paper develops a holistic model of big data analytics, GP, GSD, GSPr, GHRM and ENPr. This study is a first step in investigating how big data management contributes to ENPr in an emergency and establishing the facets of GSP as a missing link in this relationship, which is currently void in the literature. This study contributes to the theory and fills the knowledge gap in this area.
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15
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Katsaliaki K, Galetsi P, Kumar S. Supply chain disruptions and resilience: a major review and future research agenda. ANNALS OF OPERATIONS RESEARCH 2022; 319:965-1002. [PMID: 33437110 PMCID: PMC7792559 DOI: 10.1007/s10479-020-03912-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/17/2020] [Indexed: 05/05/2023]
Abstract
Our study examines the literature that has been published in important journals on supply chain disruptions, a topic that has emerged the last 20 years, with an emphasis in the latest developments in the field. Based on a review process important studies have been identified and analyzed. The content analysis of these studies synthesized existing information about the types of disruptions, their impact on supply chains, resilience methods in supply chain design and recovery strategies proposed by the studies supported by cost-benefit analysis. Our review also examines the most popular modeling approaches on the topic with indicative examples and the IT tools that enhance resilience and reduce disruption risks. Finally, a detailed future research agenda is formed about SC disruptions, which identifies the research gaps yet to be addressed. The aim of this study is to amalgamate knowledge on supply chain disruptions which constitutes an important and timely as the frequency and impact of disruptions increase. The study summarizes and builds upon the knowledge of other well-cited reviews and surveys in this research area.
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Affiliation(s)
- K. Katsaliaki
- School of Humanities, Social Sciences and Economics, International Hellenic University, 14th km Thessaloniki-N.Moudania, 57001 Thessaloniki, Greece
| | - P. Galetsi
- School of Humanities, Social Sciences and Economics, International Hellenic University, 14th km Thessaloniki-N.Moudania, 57001 Thessaloniki, Greece
| | - S. Kumar
- Opus College of Business, University of St. Thomas Minneapolis Campus, 1000 LaSalle Avenue, Schulze Hall 435, Minneapolis, MN 55403 USA
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16
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Eye tracking technology to audit google analytics: Analysing digital consumer shopping journey in fashion m-retail. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2021. [DOI: 10.1016/j.ijinfomgt.2020.102294] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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17
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Nasseef OA, Baabdullah AM, Alalwan AA, Lal B, Dwivedi YK. Artificial intelligence-based public healthcare systems: G2G knowledge-based exchange to enhance the decision-making process. GOVERNMENT INFORMATION QUARTERLY 2021. [DOI: 10.1016/j.giq.2021.101618] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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18
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Decoding digital transformational outsourcing: The role of service providers’ capabilities. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2021. [DOI: 10.1016/j.ijinfomgt.2020.102295] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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19
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Konchak CW, Krive J, Au L, Chertok D, Dugad P, Granchalek G, Livschiz E, Mandala R, McElvania E, Park C, Robicsek A, Sabatini LM, Shah NS, Kaul K. From Testing to Decision-Making: A Data-Driven Analytics COVID-19 Response. Acad Pathol 2021; 8:23742895211010257. [PMID: 33959677 PMCID: PMC8060741 DOI: 10.1177/23742895211010257] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/21/2021] [Accepted: 03/02/2021] [Indexed: 01/19/2023] Open
Abstract
In March 2020, NorthShore University Health System laboratories mobilized to
develop and validate polymerase chain reaction based testing for detection of
SARS-CoV-2. Using laboratory data, NorthShore University Health System created
the Data Coronavirus Analytics Research Team to track activities affected by
SARS-CoV-2 across the organization. Operational leaders used data insights and
predictions from Data Coronavirus Analytics Research Team to redeploy critical
care resources across the hospital system, and real-time data were used daily to
make adjustments to staffing and supply decisions. Geographical data were used
to triage patients to other hospitals in our system when COVID-19 detected
pavilions were at capacity. Additionally, one of the consequences of COVID-19
was the inability for patients to receive elective care leading to extended
periods of pain and uncertainty about a disease or treatment. After shutting
down elective surgeries beginning in March of 2020, NorthShore University Health
System set a recovery goal to achieve 80% of our historical volumes by October
1, 2020. Using the Data Coronavirus Analytics Research Team, our operational and
clinical teams were able to achieve 89% of our historical volumes a month ahead
of schedule, allowing rapid recovery of surgical volume and financial stability.
The Data Coronavirus Analytics Research Team also was used to demonstrate that
the accelerated recovery period had no negative impact with regard to iatrogenic
COVID-19 infection and did not result in increased deep vein thrombosis,
pulmonary embolisms, or cerebrovascular accident. These achievements demonstrate
how a coordinated and transparent data-driven effort that was built upon a
robust laboratory testing capability was essential to the operational response
and recovery from the COVID-19 crisis.
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Affiliation(s)
| | - Jacob Krive
- NorthShore University Health System, Evanston, IL, USA.,University of Illinois at Chicago, IL, USA.,University of Chicago, IL, USA
| | - Loretta Au
- NorthShore University Health System, Evanston, IL, USA
| | | | - Priya Dugad
- NorthShore University Health System, Evanston, IL, USA
| | | | | | | | | | | | | | | | - Nirav S Shah
- NorthShore University Health System, Evanston, IL, USA.,University of Chicago, IL, USA
| | - Karen Kaul
- NorthShore University Health System, Evanston, IL, USA.,University of Chicago, IL, USA
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20
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Alsunaidi SJ, Almuhaideb AM, Ibrahim NM, Shaikh FS, Alqudaihi KS, Alhaidari FA, Khan IU, Aslam N, Alshahrani MS. Applications of Big Data Analytics to Control COVID-19 Pandemic. SENSORS (BASEL, SWITZERLAND) 2021; 21:2282. [PMID: 33805218 PMCID: PMC8037067 DOI: 10.3390/s21072282] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 03/20/2021] [Accepted: 03/22/2021] [Indexed: 12/29/2022]
Abstract
The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.
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Affiliation(s)
- Shikah J. Alsunaidi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Abdullah M. Almuhaideb
- Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
| | - Nehad M. Ibrahim
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Fatema S. Shaikh
- Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
| | - Kawther S. Alqudaihi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Fahd A. Alhaidari
- Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
| | - Irfan Ullah Khan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Nida Aslam
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Mohammed S. Alshahrani
- Department of Emergency Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
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21
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Rousopoulou V, Nizamis A, Vafeiadis T, Ioannidis D, Tzovaras D. Predictive Maintenance for Injection Molding Machines Enabled by Cognitive Analytics for Industry 4.0. Front Artif Intell 2021; 3:578152. [PMID: 33733217 PMCID: PMC7861291 DOI: 10.3389/frai.2020.578152] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 09/24/2020] [Indexed: 11/23/2022] Open
Abstract
The exploitation of big volumes of data in Industry 4.0 and the increasing development of cognitive systems strongly facilitate the realm of predictive maintenance for real-time decisions and early fault detection in manufacturing and production. Cognitive factories of Industry 4.0 aim to be flexible, adaptive, and reliable, in order to derive an efficient production scheme, handle unforeseen conditions, predict failures, and aid the decision makers. The nature of the data streams available in industrial sites and the lack of annotated reference data or expert labels create the challenge to design augmented and combined data analytics solutions. This paper introduces a cognitive analytics, self- and autonomous-learned system bearing predictive maintenance solutions for Industry 4.0. A complete methodology for real-time anomaly detection on industrial data and its application on injection molding machines are presented in this study. Ensemble prediction models are implemented on the top of supervised and unsupervised learners and build a compound prediction model of historical data utilizing different algorithms’ outputs to a common consensus. The generated models are deployed on a real-time monitoring system, detecting faults in real-time incoming data streams. The key strength of the proposed system is the cognitive mechanism which encompasses a real-time self-retraining functionality based on a novel double-oriented evaluation objective, a data-driven and a model-based one. The presented application aims to support maintenance activities from injection molding machines’ operators and demonstrate the advances that can be offered by exploiting artificial intelligence capabilities in Industry 4.0.
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Affiliation(s)
- Vaia Rousopoulou
- Centre for Research and Technology Hellas-Information Technologies Institute (CERTH/ITI), Thessaloniki, Greece
| | - Alexandros Nizamis
- Centre for Research and Technology Hellas-Information Technologies Institute (CERTH/ITI), Thessaloniki, Greece
| | - Thanasis Vafeiadis
- Centre for Research and Technology Hellas-Information Technologies Institute (CERTH/ITI), Thessaloniki, Greece
| | - Dimosthenis Ioannidis
- Centre for Research and Technology Hellas-Information Technologies Institute (CERTH/ITI), Thessaloniki, Greece
| | - Dimitrios Tzovaras
- Centre for Research and Technology Hellas-Information Technologies Institute (CERTH/ITI), Thessaloniki, Greece
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22
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Fedorowski JJ. Could amantadine interfere with COVID-19 vaccines based on the LNP-mRNA platform? Arch Med Sci 2021; 17:827-828. [PMID: 34025855 PMCID: PMC8130463 DOI: 10.5114/aoms/134716] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 03/21/2021] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Amantadine is a well-known medication with indications in neurology and infectious diseases. It is currently FDA approved for Parkinson's disease, drug-induced extrapyramidal symptoms, and influenza. METHODS The article is the author's original research hypothesis. RESULTS Because more people are going to be vaccinated and additional similar vaccines are going to be introduced, we should take into consideration the potential of amantadine to interfere with LNP-mRNA COVID-19 vaccine delivery into the target cells. CONCLUSIONS A more cautious approach to the patients taking amantadine as far as vaccination utilizing LNP-mRNA platform should be considered.
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Affiliation(s)
- Jaroslaw J. Fedorowski
- Polish Hospital Federation, Poland
- Collegium Humanum Warsaw Management University, Warsaw, Poland
- College of Medicine and Health Network, University of Vermont, Vermont, United States
- Warsaw Maria Curie-Sklodowska Medical University, Warsaw, Poland
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Khanra S, Dhir A, Islam AKMN, Mäntymäki M. Big data analytics in healthcare: a systematic literature review. ENTERP INF SYST-UK 2020. [DOI: 10.1080/17517575.2020.1812005] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sayantan Khanra
- Turku School of Economics, University of Turku, Turku, Finland
- School of Business, Woxsen University, Hyderabad, India
| | - Amandeep Dhir
- School of Business and Management, LUT University, Lappeenranta, Finland
- Department of Management, School of Business & Law, University of Agder, Kristiansand, Norway
- Optentia Research Focus Area, North-West University, Vanderbijlpark, South Africa
| | | | - Matti Mäntymäki
- Turku School of Economics, University of Turku, Turku, Finland
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24
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Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2020. [DOI: 10.1016/j.ijinfomgt.2020.102190] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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25
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Influencing models and determinants in big data analytics research: A bibliometric analysis. Inf Process Manag 2020. [DOI: 10.1016/j.ipm.2020.102234] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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26
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Mohabatkar H, Ebrahimi S, Moradi M. Using Chou’s Five-steps Rule to Classify and Predict Glutathione S-transferases with Different Machine Learning Algorithms and Pseudo Amino Acid Composition. Int J Pept Res Ther 2020. [DOI: 10.1007/s10989-020-10087-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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27
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Big Data in Education: Perception of Training Advisors on Its Use in the Educational System. SOCIAL SCIENCES-BASEL 2020. [DOI: 10.3390/socsci9040053] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Big Data has revolutionized decision making in many fields, including education. The incorporation of information and communication technologies into education enables us to gather information about the teaching and learning process. As Big Data can help us improve it, it is paramount to integrate it into initial and continuous learning stages. This study therefore aims at finding out the perception of the training advisors of teacher training centers (N = 117) in Andalusia on the application of Big Data in education. The tool is an adaptation of the VABIDAE (Assessment of Big Data Applied to Education) scale, and the study of the descriptive statistics was carried out by using the analysis of variance (ANOVA) and Mann–Whitney U tests in order to check the existence of significant differences and correlations between the items that make up the scale. The results reflect the positive perception of training advisors on the use of Big Data in education. Significant differences were found in the competence level variable, whereby this tool was better rated by those advisors who feel that they have an advanced competence level. In conclusion, Big Data is valued for its ability to personalize educational processes and the consequent improvement in academic results, which shows the need to increase the level of knowledge about this tool.
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