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Nasri S, Fatemi M, Nazeri N, Ghazinoory S. The new framework of innovation biosphere for analysing innovation policies facing COVID-19 grand challenge. Health Res Policy Syst 2024; 22:69. [PMID: 38872202 PMCID: PMC11170802 DOI: 10.1186/s12961-024-01148-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 05/09/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Facing global grand challenges such as coronavirus disease 2019 (COVID-19) require the participation of various actors in different sectors and systematically directing their innovative efforts. Considering the complexity, non-linear dynamics, and global extent of the COVID-19 challenge, developing and applying a multi-level, resilient, and systematic innovative framework is vital. Therefore, this study aims to apply the "innovation biosphere" framework inspired by ecological studies for examining and analysing the management dimensions of COVID-19. METHODS In this research, based on a deductive-inductive approach, the case study methodology is used. In accordance with this strategy, the innovation biosphere metaphor is considered as the basic framework (deductive approach) and subsequently the grand challenge of COVID-19 (inductive approach) is analysed at three levels: micro, meso and macro. RESULTS The research findings verify the correspondence between what happened in the management of COVID-19 and the proposed framework of innovation biosphere. In other words, the findings of the research show that the effect of global cooperation, role-playing and co-evolution of different actors and subsystems in facing the grand challenge of COVID-19 under an ecosystemic and eco-innovation approach has been evident. These events subsequently led to the cessation of the pandemic after about four years. CONCLUSIONS The main policy implications include the role of self-organization, the capability of global value networks, mission orientation, and co-evolution between actors as the contributions of innovation biosphere framework for managing grand health challenges, and global cohesion, oligopoly market, supporting local innovations, the critical role of basic research, and deregulation as the contributions of the COVID-19 case study for enhancing the innovation biosphere metaphor.
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Affiliation(s)
- Shohreh Nasri
- Department of Science & Research Policy, National Research Institute for Science Policy, Tehran, Iran.
| | - Mehdi Fatemi
- Department of Information Technology Management, Tarbiat Modares University, Tehran, Iran
| | | | - Sepehr Ghazinoory
- Department of Information Technology Management, Tarbiat Modares University, Tehran, Iran
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Tarek Z, Shams MY, Towfek SK, Alkahtani HK, Ibrahim A, Abdelhamid AA, Eid MM, Khodadadi N, Abualigah L, Khafaga DS, Elshewey AM. An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction. Biomimetics (Basel) 2023; 8:552. [PMID: 37999193 PMCID: PMC10669113 DOI: 10.3390/biomimetics8070552] [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: 06/29/2023] [Revised: 11/05/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023] Open
Abstract
The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.
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Affiliation(s)
- Zahraa Tarek
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35561, Egypt;
| | - Mahmoud Y. Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt;
| | - S. K. Towfek
- Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA;
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt;
| | - Hend K. Alkahtani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - Abdelaziz A. Abdelhamid
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt;
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
| | - Marwa M. Eid
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt;
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
| | - Nima Khodadadi
- Department of Civil and Architectural Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL 33146, USA;
| | - Laith Abualigah
- Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan;
- College of Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
- School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Ahmed M. Elshewey
- Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43512, Egypt;
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Chatterjee A, Prinz A, Riegler MA, Das J. A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care. BMC Health Serv Res 2023; 23:1047. [PMID: 37777722 PMCID: PMC10543863 DOI: 10.1186/s12913-023-10047-z] [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: 02/25/2023] [Accepted: 09/20/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients. OBJECTIVE The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic. METHODS The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients. RESULTS Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with the inclusion and exclusion criteria. The review identified the following eHealth methods-Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using a semantic knowledge graph (e.g., ontology model). CONCLUSIONS Various e-Health methods, related remote monitoring technologies, different approaches, information categories, the adoption of ICT tools for an automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICTs. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.
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Affiliation(s)
- Ayan Chatterjee
- Department of Information and Communication Technology, Centre for e-Health, University of Agder, Grimstad, Norway.
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
| | - Andreas Prinz
- Department of Information and Communication Technology, Centre for e-Health, University of Agder, Grimstad, Norway
| | - Michael A Riegler
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - Jishnu Das
- Department of Information Systems, Centre for e-Health, University of Agder, Kristiansand, Norway
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Yang DM, Chang TJ, Hung KF, Wang ML, Cheng YF, Chiang SH, Chen MF, Liao YT, Lai WQ, Liang KH. Smart healthcare: A prospective future medical approach for COVID-19. J Chin Med Assoc 2023; 86:138-146. [PMID: 36227021 PMCID: PMC9847685 DOI: 10.1097/jcma.0000000000000824] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
COVID-19 has greatly affected human life for over 3 years. In this review, we focus on smart healthcare solutions that address major requirements for coping with the COVID-19 pandemic, including (1) the continuous monitoring of severe acute respiratory syndrome coronavirus 2, (2) patient stratification with distinct short-term outcomes (eg, mild or severe diseases) and long-term outcomes (eg, long COVID), and (3) adherence to medication and treatments for patients with COVID-19. Smart healthcare often utilizes medical artificial intelligence (AI) and cloud computing and integrates cutting-edge biological and optoelectronic techniques. These are valuable technologies for addressing the unmet needs in the management of COVID. By leveraging deep learning/machine learning capabilities and big data, medical AI can perform precise prognosis predictions and provide reliable suggestions for physicians' decision-making. Through the assistance of the Internet of Medical Things, which encompasses wearable devices, smartphone apps, internet-based drug delivery systems, and telemedicine technologies, the status of mild cases can be continuously monitored and medications provided at home without the need for hospital care. In cases that develop into severe cases, emergency feedback can be provided through the hospital for rapid treatment. Smart healthcare can possibly prevent the development of severe COVID-19 cases and therefore lower the burden on intensive care units.
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Affiliation(s)
- De-Ming Yang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Biophotonics, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Address correspondence. Dr. De-Ming Yang, Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail address: (D.-M. Yang). and Dr. Kung-Hao Liang, Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail: (K.-H. Liang)
| | - Tai-Jay Chang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Laboratory of Genome Research, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Biomedical science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Kai-Feng Hung
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Mong-Lien Wang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Yen-Fu Cheng
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Su-Hua Chiang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Mei-Fang Chen
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Yi-Ting Liao
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Food Safety and Health Risk Assessment, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Wei-Qun Lai
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Biophotonics, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Kung-Hao Liang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Food Safety and Health Risk Assessment, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Address correspondence. Dr. De-Ming Yang, Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail address: (D.-M. Yang). and Dr. Kung-Hao Liang, Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail: (K.-H. Liang)
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Management of Smart and Sustainable Cities in the Post-COVID-19 Era: Lessons and Implications. SUSTAINABILITY 2022. [DOI: 10.3390/su14127267] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Nowadays, the concept of smart sustainable governance is wrapped around basic principles such as: (i) transparency, (ii) accountability, (iii) stakeholders’ involvement, and iv) citizens’ participation. It is through these principles that are influenced by information and communication technologies (ICT), Internet of Things (IoT), and artificial intelligence, that the practices employed by citizens and their interaction with electronic government (e-government) are diversified. Previously, the misleading concepts of the smart city implied only the objective of the local level or public officials to utilize technology. However, the recent European experience and research studies have led to a more comprehensive notion that refers to the search for intelligent solutions which allow modern sustainable cities to enhance the quality of services provided to citizens and to improve the management of urban mobility. The smart city is based on the usage of connected sensors, data management, and analytics platforms to improve the quality and functioning of built-environment systems. The aim of this paper is to understand the effects of the pandemic on smart cities and to accentuate major exercises that can be learned for post-COVID sustainable urban management and patterns. The lessons and implications outlined in this paper can be used to enforce social distancing community measures in an effective and timely way, and to optimize the use of resources in smart and sustainable cities in critical situations. The paper offers a conceptual overview and serves as a stepping-stone to extensive research and the deployment of sustainable smart city platforms and intelligent transportation systems (a sub-area of smart city applications) after the COVID-19 pandemic using a case study from Russia. Overall, our results demonstrate that the COVID-19 crisis encompasses an excellent opportunity for urban planners and policy makers to take transformative actions towards creating cities that are more intelligent and sustainable.
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The internet of medical things and artificial intelligence: trends, challenges, and opportunities. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Pattnaik SK, Samal SR, Bandopadhaya S, Swain K, Choudhury S, Das JK, Mihovska A, Poulkov V. Future Wireless Communication Technology towards 6G IoT: An Application-Based Analysis of IoT in Real-Time Location Monitoring of Employees Inside Underground Mines by Using BLE. SENSORS 2022; 22:s22093438. [PMID: 35591138 PMCID: PMC9103828 DOI: 10.3390/s22093438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 04/27/2022] [Accepted: 04/27/2022] [Indexed: 02/06/2023]
Abstract
In recent years, the IoT has emerged as the most promising technology in the key evolution of industry 4.0/industry 5.0, smart home automation (SHA), smart cities, energy savings and many other areas of wireless communication. There is a massively growing number of static and mobile IoT devices with a diversified range of speed and bandwidth, along with a growing demand for high data rates, which makes the network denser and more complicated. In this context, the next-generation communication technology, i.e., sixth generation (6G), is trying to build up the base to meet the imperative need of future network deployment. This article adopts the vision for 6G IoT systems and proposes an IoT-based real-time location monitoring system using Bluetooth Low Energy (BLE) for underground communication applications. An application-based analysis of industrial positioning systems is also presented.
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Affiliation(s)
- Sushant Kumar Pattnaik
- School of Electronics Engineering, KIIT University, Bhubaneswar 751024, India; (S.K.P.); (J.K.D.)
| | - Soumya Ranjan Samal
- Faculty of Telecommunications, Technical University of Sofia, 1756 Sofia, Bulgaria;
- Department of Electronics & Communication Engineering, Silicon Institute of Technology, Bhubaneswar 751024, India
- Correspondence:
| | | | - Kaliprasanna Swain
- Department of Electronics & Communication Engineering, Gandhi Institute for Technological Advancements, Bhubaneswar 752054, India;
| | - Subhashree Choudhury
- Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan, Bhubaneswar 751030, India;
| | - Jitendra Kumar Das
- School of Electronics Engineering, KIIT University, Bhubaneswar 751024, India; (S.K.P.); (J.K.D.)
| | - Albena Mihovska
- Department of Business Development & Technologies, Aarhus University, 8000 Aarhus, Denmark;
| | - Vladimir Poulkov
- Faculty of Telecommunications, Technical University of Sofia, 1756 Sofia, Bulgaria;
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