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Lewis KO, Popov V, Fatima SS. From static web to metaverse: reinventing medical education in the post-pandemic era. Ann Med 2024; 56:2305694. [PMID: 38261592 PMCID: PMC10810636 DOI: 10.1080/07853890.2024.2305694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/06/2024] [Indexed: 01/25/2024] Open
Abstract
The World Wide Web and the advancement of computer technology in the 1960s and 1990s respectively set the ground for a substantial and simultaneous change in many facets of our life, including medicine, health care, and medical education. The traditional didactic approach has shifted towards more dynamic and interactive methods, leveraging technologies such as simulation tools, virtual reality, and online platforms. At the forefront is the remarkable evolution that has revolutionized how medical knowledge is accessed, disseminated, and integrated into pedagogical practices. The COVID-19 pandemic also led to rapid and large-scale adoption of e-learning and digital resources in medical education because of widespread lockdowns, social distancing measures, and the closure of medical schools and healthcare training programs. This review paper examines the evolution of medical education from the Flexnerian era to the modern digital age, closely examining the influence of the evolving WWW and its shift from Education 1.0 to Education 4.0. This evolution has been further accentuated by the transition from the static landscapes of Web 2D to the immersive realms of Web 3D, especially considering the growing notion of the metaverse. The application of the metaverse is an interconnected, virtual shared space that includes virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create a fertile ground for simulation-based training, collaborative learning, and experiential skill acquisition for competency development. This review includes the multifaceted applications of the metaverse in medical education, outlining both its benefits and challenges. Through insightful case studies and examples, it highlights the innovative potential of the metaverse as a platform for immersive learning experiences. Moreover, the review addresses the role of emerging technologies in shaping the post-pandemic future of medical education, ultimately culminating in a series of recommendations tailored for medical institutions aiming to successfully capitalize on revolutionary changes.
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Affiliation(s)
- Kadriye O. Lewis
- Children’s Mercy Kansas City, Department of Pediatrics, UMKC School of Medicine, Kansas City, MO, USA
| | - Vitaliy Popov
- Department of Learning Health Sciences, University of MI Medical School, Ann Arbor, MI, USA
| | - Syeda Sadia Fatima
- Department of Biological and Biomedical Sciences, The Aga Khan University, Karachi, Pakistan
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2
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Jovanovic L, Damaševičius R, Matic R, Kabiljo M, Simic V, Kunjadic G, Antonijevic M, Zivkovic M, Bacanin N. Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics. PeerJ Comput Sci 2024; 10:e2031. [PMID: 38855236 PMCID: PMC11157549 DOI: 10.7717/peerj-cs.2031] [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: 01/19/2024] [Accepted: 04/09/2024] [Indexed: 06/11/2024]
Abstract
Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.
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Affiliation(s)
- Luka Jovanovic
- Faculty of Technical Sciences, Singidunum University, Belgrade, Serbia
| | | | - Rade Matic
- Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Belgrade, Serbia
| | - Milos Kabiljo
- Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Belgrade, Serbia
| | - Vladimir Simic
- Faculty of Transport and Traffic Engineering, University of Belgrade, Belgrade, Serbia
- College of Engineering, Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan City, Taiwan
| | - Goran Kunjadic
- Higher Colleges of Technology, Abu Dhabi, United Arab Emirates
| | - Milos Antonijevic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Miodrag Zivkovic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Nebojsa Bacanin
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
- MEU Research Unit, Middle East University, Amman, Jordan
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3
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Sornalakshmi M, Devakanth JJMA, Rajalakshmi R, Velmurugadass P. An energy-aware heart disease prediction system using ESMO and optimal deep learning model for healthcare monitoring in IoT. J Biomol Struct Dyn 2024:1-15. [PMID: 38165748 DOI: 10.1080/07391102.2023.2298736] [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: 08/13/2023] [Accepted: 12/18/2023] [Indexed: 01/04/2024]
Abstract
The Internet of Things (IoT), which provides seamless connectivity between people and things, improves our quality of life. In the medical field, predictive analytics can help transform a reactive healthcare (HC) strategy into a proactive one. The HC industry embraces cutting-edge artificial intelligence and machine learning (ML) technologies. ML's area of deep learning has the revolutionary potential to reliably analyze massive volumes of data quickly, produce insightful revelations and solve challenging issues. This article proposes an energy-aware heart disease prediction (HDP) system based on enhanced spider monkey optimization (ESMO) and a weight-optimized neural network for an IoT-based HC environment. The proposed work consists of two essential phases: energy-efficient data transmission and HDP. In energy-efficient transmission, the cluster leaders are optimally selected using ESMO and the cluster formation is done based on Euclidean distance. In HDP, the patient data are collected from the dataset, and essential features are extracted. After that, the dimensionality reduction is carried out using the modified linear discriminant analysis approach to reduce over-fitting issues. Finally, the HDP uses the enhanced Archimedes weight-optimized deep neural network (EAWO-DNN). The simulation findings demonstrate that the proposed optimal clustering mechanism enhances the network's lifespan by consuming minimal energy compared to the existing techniques. Also, the proposed EAWO-DNN classifier achieves higher prediction accuracy, precision, recall and f-measure than the conventional methods for predicting heart disease in IoT.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- M Sornalakshmi
- PG Department of Computer Science, Arulmigu Kalasalingam College of Arts and Science, Krishnan Koil, Tamil Nadu, India
| | - J Jude Moses Anto Devakanth
- Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
| | - R Rajalakshmi
- Department of Electronics and Communication Engineering, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India
| | - P Velmurugadass
- Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnan Koil, Tamil Nadu, India
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4
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Shaikh TA, Rasool T, Verma P. Machine intelligence and medical cyber-physical system architectures for smart healthcare: Taxonomy, challenges, opportunities, and possible solutions. Artif Intell Med 2023; 146:102692. [PMID: 38042609 DOI: 10.1016/j.artmed.2023.102692] [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: 05/15/2023] [Revised: 10/21/2023] [Accepted: 10/22/2023] [Indexed: 12/04/2023]
Abstract
Hospitals use medical cyber-physical systems (MCPS) more often to give patients quality continuous care. MCPS isa life-critical, context-aware, networked system of medical equipment. It has been challenging to achieve high assurance in system software, interoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability due to the necessity to create complicated MCPS that are safe and efficient. The MCPS system is shown in the paper as a newly developed application case study of artificial intelligence in healthcare. Applications for various CPS-based healthcare systems are discussed, such as telehealthcare systems for managing chronic diseases (cardiovascular diseases, epilepsy, hearing loss, and respiratory diseases), supporting medication intake management, and tele-homecare systems. The goal of this study is to provide a thorough overview of the essential components of the MCPS from several angles, including design, methodology, and important enabling technologies, including sensor networks, the Internet of Things (IoT), cloud computing, and multi-agent systems. Additionally, some significant applications are investigated, such as smart cities, which are regarded as one of the key applications that will offer new services for industrial systems, transportation networks, energy distribution, monitoring of environmental changes, business and commerce applications, emergency response, and other social and recreational activities.The four levels of an MCPS's general architecture-data collecting, data aggregation, cloud processing, and action-are shown in this study. Different encryption techniques must be employed to ensure data privacy inside each layer due to the variations in hardware and communication capabilities of each layer. We compare established and new encryption techniques based on how well they support safe data exchange, secure computing, and secure storage. Our thorough experimental study of each method reveals that, although enabling innovative new features like secure sharing and safe computing, developing encryption approaches significantly increases computational and storage overhead. To increase the usability of newly developed encryption schemes in an MCPS and to provide a comprehensive list of tools and databases to assist other researchers, we provide a list of opportunities and challenges for incorporating machine intelligence-based MCPS in healthcare applications in our paper's conclusion.
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Affiliation(s)
- Tawseef Ayoub Shaikh
- Department of Computer Science & Engineering, National Institute of Technology (NIT), Srinagar 190006, Jammu & Kashmir, India.
| | - Tabasum Rasool
- NPDF Fellow, Interdisciplinary Centre for Water Research (ICWaR), Indian Institute of Science, Bengaluru, India.
| | - Prabal Verma
- Department of Information Technology, National Institute of Technology (NIT), Srinagar 190006, Jammu & Kashmir, India.
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Hossain MM, Kashem MA, Islam MM, Sahidullah M, Mumu SH, Uddin J, Aray DG, de la Torre Diez I, Ashraf I, Samad MA. Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:9367. [PMID: 38067740 PMCID: PMC10708762 DOI: 10.3390/s23239367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 11/17/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023]
Abstract
The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals.
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Affiliation(s)
- Mohammad Mobarak Hossain
- Department of Computer Science and Engineering, Dhaka University of Engineering and Technology (DUET), Gazipur 1707, Bangladesh; (M.M.H.); (M.A.K.)
| | - Mohammod Abul Kashem
- Department of Computer Science and Engineering, Dhaka University of Engineering and Technology (DUET), Gazipur 1707, Bangladesh; (M.M.H.); (M.A.K.)
| | - Md. Monirul Islam
- Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka 1216, Bangladesh;
| | - Md. Sahidullah
- Department of Computer Science and Engineering, Asian University of Bangladesh (AUB), Bangabandhu Road, Tongabari Ashulia, Dhaka 1349, Bangladesh
| | - Sumona Hoque Mumu
- School of Kinesiology, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Jia Uddin
- AI and Big Data Department, Endicott College, Woosong University, Daejeon 34606, Republic of Korea;
| | - Daniel Gavilanes Aray
- Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
| | - Isabel de la Torre Diez
- Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Md Abdus Samad
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Suleski T, Ahmed M. A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things. J Med Internet Res 2023; 25:e44114. [PMID: 37490633 PMCID: PMC10498322 DOI: 10.2196/44114] [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: 11/07/2022] [Revised: 03/16/2023] [Accepted: 07/23/2023] [Indexed: 07/27/2023] Open
Abstract
The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The systems of interconnected devices, users, data, and working environments are referred to as the Internet of Health Care Things (IoHT). IoHT devices have emerged in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources. These devices cater to the need for remote health care services outside of physical interactions. However, IoHT security is often overlooked because the devices are quickly deployed and configured as solutions to meet the demands of a heavily saturated industry. During the COVID-19 pandemic, studies have shown that cybercriminals are exploiting the health care industry, and data breaches are targeting user credentials through authentication vulnerabilities. Poor password use and management and the lack of multifactor authentication security posture within IoHT cause a loss of millions according to the IBM reports. Therefore, it is important that health care authentication security moves toward adaptive multifactor authentication (AMFA) to replace the traditional approaches to authentication. We identified a lack of taxonomy for data models that particularly focus on IoHT data architecture to improve the feasibility of AMFA. This viewpoint focuses on identifying key cybersecurity challenges in a theoretical framework for a data model that summarizes the main components of IoHT data. The data are to be used in modalities that are suited for health care users in modern IoHT environments and in response to the COVID-19 pandemic. To establish the data taxonomy, a review of recent IoHT papers was conducted to discuss the related work in IoHT data management and use in next-generation authentication systems. Reports, journal articles, conferences, and white papers were reviewed for IoHT authentication data technologies in relation to the problem statement of remote authentication and user management systems. Only publications written in English from the last decade were included (2012-2022) to identify key issues within the current health care practices and their management of IoHT devices. We discuss the components of the IoHT architecture from the perspective of data management and sensitivity to ensure privacy for all users. The data model addresses the security requirements of IoHT users, environments, and devices toward the automation of AMFA in health care. We found that in health care authentication, the significant threats occurring were related to data breaches owing to weak security options and poor user configuration of IoHT devices. The security requirements of IoHT data architecture and identified impactful methods of cybersecurity for health care devices, data, and their respective attacks are discussed. Data taxonomy provides better understanding, solutions, and improvements of user authentication in remote working environments for security features.
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Affiliation(s)
- Tance Suleski
- School of Science, Edith Cowan University, Perth, Australia
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7
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Chataut R, Phoummalayvane A, Akl R. Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0. SENSORS (BASEL, SWITZERLAND) 2023; 23:7194. [PMID: 37631731 PMCID: PMC10458191 DOI: 10.3390/s23167194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/02/2023] [Accepted: 07/04/2023] [Indexed: 08/27/2023]
Abstract
The Internet of Things (IoT) technology and devices represent an exciting field in computer science that is rapidly emerging worldwide. The demand for automation and efficiency has also been a contributing factor to the advancements in this technology. The proliferation of IoT devices coincides with advancements in wireless networking technologies, driven by the enhanced connectivity of the internet. Today, nearly any everyday object can be connected to the network, reflecting the growing demand for automation and efficiency. This paper reviews the emergence of IoT devices, analyzed their common applications, and explored the future prospects in this promising field of computer science. The examined applications encompass healthcare, agriculture, and smart cities. Although IoT technology exhibits similar deployment trends, this paper will explore different fields to discern the subtle nuances that exist among them. To comprehend the future of IoT, it is essential to comprehend the driving forces behind its advancements in various industries. By gaining a better understanding of the emergence of IoT devices, readers will develop insights into the factors that have propelled their growth and the conditions that led to technological advancements. Given the rapid pace at which IoT technology is advancing, this paper provides researchers with a deeper understanding of the factors that have brought us to this point and the ongoing efforts that are actively shaping the future of IoT. By offering a comprehensive analysis of the current landscape and potential future developments, this paper serves as a valuable resource to researchers seeking to contribute to and navigate the ever-evolving IoT ecosystem.
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Affiliation(s)
- Robin Chataut
- School of Computing and Engineering, Quinnipiac University, Hamden, CT 06518, USA
| | - Alex Phoummalayvane
- Computer Science Department, Fitchburg State University, Fitchburg, MA 01420, USA;
| | - Robert Akl
- Department of Computer Science, University of North University, Denton, TX 76203, USA;
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Waleed M, Kamal T, Um TW, Hafeez A, Habib B, Skouby KE. Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges. SENSORS (BASEL, SWITZERLAND) 2023; 23:6760. [PMID: 37571543 PMCID: PMC10422369 DOI: 10.3390/s23156760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/17/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023]
Abstract
The remote monitoring of patients using the internet of things (IoT) is essential for ensuring continuous observation, improving healthcare, and decreasing the associated costs (i.e., reducing hospital admissions and emergency visits). There has been much emphasis on developing methods and approaches for remote patient monitoring using IoT. Most existing frameworks cover parts or sub-parts of the overall system but fail to provide a detailed and well-integrated model that covers different layers. The leverage of remote monitoring tools and their coupling with health services requires an architecture that handles data flow and enables significant interventions. This paper proposes a cloud-based patient monitoring model that enables IoT-generated data collection, storage, processing, and visualization. The system has three main parts: sensing (IoT-enabled data collection), network (processing functions and storage), and application (interface for health workers and caretakers). In order to handle the large IoT data, the sensing module employs filtering and variable sampling. This pre-processing helps reduce the data received from IoT devices and enables the observation of four times more patients compared to not using edge processing. We also discuss the flow of data and processing, thus enabling the deployment of data visualization services and intelligent applications.
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Affiliation(s)
- Muhammad Waleed
- Department of Electronic Systems, Aalborg University Copenhagen, 2450 København, Denmark;
| | - Tariq Kamal
- Electrical and Computer Engineering, Habib University, Karachi 75290, Pakistan
| | - Tai-Won Um
- Graduate School of Data Science, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Abdul Hafeez
- Computer Science and Applications, Virginia Tech, Blacksburg, VA 24061, USA
| | - Bilal Habib
- Department of Computer Systems Engineering, University of Engineering and Technology (UET), Peshawar 25120, Pakistan
| | - Knud Erik Skouby
- Department of Electronic Systems, Aalborg University Copenhagen, 2450 København, Denmark;
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Dhopte A, Bagde H. Smart Smile: Revolutionizing Dentistry With Artificial Intelligence. Cureus 2023; 15:e41227. [PMID: 37529520 PMCID: PMC10387377 DOI: 10.7759/cureus.41227] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative technology in various industries, and its potential in dentistry is gaining significant attention. This abstract explores the future prospects of AI in dentistry, highlighting its potential to revolutionize clinical practice, improve patient outcomes, and enhance the overall efficiency of dental care. The application of AI in dentistry encompasses several key areas, including diagnosis, treatment planning, image analysis, patient management, and personalized care. AI algorithms have shown promising results in the automated detection and diagnosis of dental conditions, such as caries, periodontal diseases, and oral cancers, aiding clinicians in early intervention and improving treatment outcomes. Furthermore, AI-powered treatment planning systems leverage machine learning techniques to analyze vast amounts of patient data, considering factors like medical history, anatomical variations, and treatment success rates. These systems provide dentists with valuable insights and support in making evidence-based treatment decisions, ultimately leading to more predictable and tailored treatment approaches. While the potential of AI in dentistry is immense, it is essential to address certain challenges, including data privacy, algorithm bias, and regulatory considerations. Collaborative efforts between dental professionals, AI experts, and policymakers are crucial to developing robust frameworks that ensure the responsible and ethical implementation of AI in dentistry. Moreover, AI-driven robotics has introduced innovative approaches to dental surgery, enabling precise and minimally invasive procedures, and ultimately reducing patient discomfort and recovery time. Virtual reality (VR) and augmented reality (AR) applications further enhance dental education and training, allowing dental professionals to refine their skills in a realistic and immersive environment. AI holds tremendous promise in shaping the future of dentistry. Through its ability to analyze vast amounts of data, provide accurate diagnoses, facilitate treatment planning, improve image analysis, streamline patient management, and enable personalized care, AI has the potential to enhance dental practice and significantly improve patient outcomes. Embracing this technology and its future development will undoubtedly revolutionize the field of dentistry, fostering a more efficient, precise, and patient-centric approach to oral healthcare. Overall, AI represents a powerful tool that has the potential to revolutionize various aspects of society, from improving healthcare outcomes to optimizing business operations. Continued research, development, and responsible implementation of AI technologies will shape our future, unlocking new possibilities and transforming the way we live and work.
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Affiliation(s)
- Ashwini Dhopte
- Department of Oral Medicine and Radiology, Rama Dental College and Research Centre, Kanpur, IND
| | - Hiroj Bagde
- Department of Periodontology, Rama Dental College and Research Centre, Kanpur, IND
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Almalawi A, Khan AI, Alsolami F, Abushark YB, Alfakeeh AS. Managing Security of Healthcare Data for a Modern Healthcare System. SENSORS (BASEL, SWITZERLAND) 2023; 23:3612. [PMID: 37050672 PMCID: PMC10098823 DOI: 10.3390/s23073612] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 03/19/2023] [Accepted: 03/23/2023] [Indexed: 06/19/2023]
Abstract
The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data. Therefore, this research proposes a novel Lionized remora optimization-based serpent (LRO-S) encryption method to encrypt sensitive data and reduce privacy breaches and cyber-attacks from unauthorized users and hackers. The LRO-S method is the combination of hybrid metaheuristic optimization and improved security algorithm. The fitness functions of lion and remora are combined to create a new algorithm for security key generation, which is provided to the serpent encryption algorithm. The LRO-S technique encrypts sensitive patient data before storing it in the cloud. The primary goal of this study is to improve the safety and adaptability of medical professionals' access to cloud-based patient-sensitive data more securely. The experiment's findings suggest that the secret keys generated are sufficiently random and one of a kind to provide adequate protection for the data stored in modern healthcare management systems. The proposed method minimizes the time needed to encrypt and decrypt data and improves privacy standards. This study found that the suggested technique outperformed previous techniques in terms of reducing execution time and is cost-effective.
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Affiliation(s)
- Abdulmohsen Almalawi
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Asif Irshad Khan
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Fawaz Alsolami
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Yoosef B. Abushark
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Ahmed S. Alfakeeh
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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A Statistical Synopsis of COVID-19 Components and Descriptive Analysis of Their Socio-Economic and Healthcare Aspects in Bangladesh Perspective. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2023; 2023:9738094. [PMID: 36815185 PMCID: PMC9940984 DOI: 10.1155/2023/9738094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/02/2023] [Accepted: 01/24/2023] [Indexed: 02/16/2023]
Abstract
The aim of the work is to analyze the socio-economic and healthcare aspects that arise in the contemporary COVID-19 situation from Bangladesh perspective. We elaborately discuss the successive COVID-19 occurrences in Bangladesh with consequential information. The components associated with the COVID-19 commencement and treatment policy with corresponding features and their consequences are patently delineated. The effect of troublesome issues related to the treatment is detailed with supporting real-time data. We elucidate the applications of modern technologies advancement in epidemiological aspects and their existent compatibility in Bangladesh. We statistically analyze the real-time data through figurative and tabular approaches. Some relevant measures of central tendency and dispersion are utilized to explore the data structure and its observable specifications. For a clear manifestation, Z- scores of the COVID-19 components are analyzed through the Box-Whisker plot. We have discovered that the gathered data exhibit features that are unsatisfactory for the normal distribution, are highly positively skewed, and are predominated by the earliest occurrences. Infections and deaths were initially lower than the global average, but they drastically rose in the first quarter of 2021 and persisted for the remainder of the year. Substantial preventive results were produced by the region-wisetime-worthy moves. In the fourth quarter of 2021, the infections and deaths noticeably decreased, and the number of recoveries was highly significant. In the middle of 2022, a lethal rise in infections was observed in Bangladesh and that was quickly stabilized, and the pandemic ingredients were under control. According to our assessment, some concluding remarks are made at the end of this work.
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Ren S, Cao W, Ma J, Li H, Xia Y, Zhao J. Correlation evaluation between cancer microenvironment related genes and prognosis based on intelligent medical internet of things. Front Genet 2023; 14:1132242. [PMID: 36845384 PMCID: PMC9947234 DOI: 10.3389/fgene.2023.1132242] [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: 12/27/2022] [Accepted: 01/24/2023] [Indexed: 02/11/2023] Open
Abstract
The study of tumor microenvironment plays an important role in the treatment of cancer patients. In this paper, intelligent medical Internet of Things technology was used to analyze cancer tumor microenvironment-related genes. Through experiments designed and analyzed cancer-related genes, this study concluded that in cervical cancer, patients with high expression of P16 gene had a shorter life cycle and a survival rate of 35%. In addition, through investigation and interview, it was found that patients with positive expression of P16 and Twist genes had a higher recurrence rate than patients with negative expression of both genes; high expression of FDFT1, AKR1C1, and ALOX12 in colon cancer is associated with short survival; high expressions of HMGCR and CARS1 is associated with longer survival; overexpression of NDUFA12, FD6, VEZT, GDF3, PDE5A, GALNTL6, OPMR1, and AOAH in thyroid cancer is associated with shortened survival; high expressions of NR2C1, FN1, IPCEF1, and ELMO1 is associated with prolonged survival. Among the genes associated with the prognosis of liver cancer, the genes associated with shorter survival period are AGO2, DCPS, IFIT5, LARP1, NCBP2, NUDT10, and NUDT16; the genes associated with longevity are EIF4E3, EIF4G3, METTL1, NCBP1, NSUN2, NUDT11, NUDT4, and WDR4. Depending on the prognostic role of genes in different cancers, they can influence patients to achieve the effect of reducing patients' symptoms. In the process of disease analysis of cancer patients, this paper uses bioinformation technology and Internet of things technology to promote the development of medical intelligence.
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Affiliation(s)
- Shoulei Ren
- Oncology Department, Yangguangronghe Hospital, Weifang, Shandong, China
| | - Wenli Cao
- Oncology Department, Yangguangronghe Hospital, Weifang, Shandong, China
| | - Jianzeng Ma
- Oncology Department, Yangguangronghe Hospital, Weifang, Shandong, China
| | - Hongchun Li
- Nerosurgery Department, Yangguangronghe Hospital, Weifang, Shandong, China
| | - Yutao Xia
- Oncology Department, Yangguangronghe Hospital, Weifang, Shandong, China
| | - Jianwen Zhao
- Oncology Department, Yangguangronghe Hospital, Weifang, Shandong, China,*Correspondence: Jianwen Zhao,
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13
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Khamis AA, Idris A, Abdellatif A, Mohd Rom NA, Khamis T, Ab Karim MS, Janasekaran S, Abd Rashid RB. Development and Performance Evaluation of an IoT-Integrated Breath Analyzer. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1319. [PMID: 36674075 PMCID: PMC9859467 DOI: 10.3390/ijerph20021319] [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: 12/12/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
Although alcohol consumption may produce effects that can be beneficial or harmful, alcohol consumption prevails among communities around the globe. Additionally, alcohol consumption patterns may be associated with several factors among communities and individuals. Numerous technologies and methods are implemented to enhance the detection and tracking of alcohol consumption, such as vehicle-integrated and wearable devices. In this paper, we present a cellular-based Internet of Things (IoT) implementation in a breath analyzer to enable data collection from multiple users via a single device. Cellular technology using hypertext transfer protocol (HTTP) was implemented as an IoT gateway. IoT integration enabled the direct retrieval of information from a database relative to the device and direct upload of data from the device onto the database. A manually developed threshold algorithm was implemented to quantify alcohol concentrations within a range from 0 to 200 mcg/100 mL breath alcohol content using electrochemical reactions in a fuel-cell sensor. Two data collections were performed: one was used for the development of the model and was split into two sets for model development and on-machine validation, and another was used as an experimental verification test. An overall accuracy of 98.16% was achieved, and relative standard deviations within the range from 1.41% to 2.69% were achieved, indicating the reliable repeatability of the results. The implication of this paper is that the developed device (an IoT-integrated breath analyzer) may provide practical assistance for healthcare representatives and researchers when conducting studies involving the detection and data collection of alcohol consumption patterns.
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Affiliation(s)
- Abd Alghani Khamis
- Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Aida Idris
- Department of Management, Faculty of Business and Economics, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Abdallah Abdellatif
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | | | - Taha Khamis
- Center for Applied Biomechanics (CAB), Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Mohd Sayuti Ab Karim
- Centre of Advanced Manufacturing and Material Processing (AMMP), Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Shamini Janasekaran
- Centre for Advanced Materials and Intelligent Manufacturing, Faculty of Engineering, Built Environment & IT, SEGi University Sdn Bhd, Petaling Jaya 47810, Malaysia
| | - Rusdi Bin Abd Rashid
- Department of Psychological Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
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14
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Lovis C, Hefner J, Chen C, Huang Y, Wang X, Yang Q, Zhu X, Zhang X, Hao M, Shui L. Developing a Capsule Clinic-A 24-Hour Institution for Improving Primary Health Care Accessibility: Evidence From China. JMIR Med Inform 2023; 11:e41212. [PMID: 36622737 PMCID: PMC9871876 DOI: 10.2196/41212] [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: 07/19/2022] [Revised: 11/18/2022] [Accepted: 12/08/2022] [Indexed: 01/10/2023] Open
Abstract
Telehealth is an effective combination of medical service and intelligent technology. It can improve the problem of remote access to medical care. However, an imbalance in the allocation of health resources still occurs. People spend more time and money to access higher-quality services, which results in inequitable access to primary health care (PHC). At the same time, patients' usage of telehealth services is limited by the equipment and their own knowledge, and the PHC service suffers from low usage efficiency and lack of service supply. Therefore, improving PHC accessibility is crucial to narrowing the global health care coverage gap and maintaining health equity. In recent years, China has explored several new approaches to improve PHC accessibility. One such approach is the capsule clinic, an emerging institution that represents an upgraded version of the internet hospital. In coordination with the United Nations, the Yinzhou district of Ningbo city in Zhejiang, China, has been testing this new model since 2020. As of October 2022, the number of applications in Ningbo was 15, and the number of users reached 12,219. Unlike internet hospitals, the entire process-from diagnosis to prescription services-can be completed at the capsule clinic. The 24-hour telehealth service could also solve transportation problems and save time for users. Big data analysis can accurately identify regional populations' PHC service needs and improve efficiency in health resource allocation. The user-friendly, low-cost, and easily accessible telehealth model is of great significance. Installation of capsule clinics would improve PHC accessibility and resolve the uneven distribution of health resources to promote health equity.
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Affiliation(s)
| | | | - Chun Chen
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Yunyun Huang
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Xiaoyi Wang
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Qingren Yang
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Xuebo Zhu
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Xiangyang Zhang
- Engineering Research Center of Intelligent Medicine (2016E10011), The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mo Hao
- School of Public Health, Fudan University, Shanghai, China
| | - Liming Shui
- Yinzhou District Health Bureau, Ningbo, China
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15
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Sun H, Zhang Y, Gao G, Wu D. Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives. Front Public Health 2022; 10:958835. [PMID: 36544794 PMCID: PMC9760721 DOI: 10.3389/fpubh.2022.958835] [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: 06/01/2022] [Accepted: 11/09/2022] [Indexed: 12/12/2022] Open
Abstract
With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance.
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Affiliation(s)
- Hua Sun
- Popsmart Technology (Zhejiang) Co., Ltd, Ningbo, China
| | - Yuzhou Zhang
- Popsmart Technology (Zhejiang) Co., Ltd, Ningbo, China,College of Computer Science and Technology, Zhejiang University, Hangzhou, China,*Correspondence: Yuzhou Zhang
| | - Guang Gao
- Popsmart Technology (Zhejiang) Co., Ltd, Ningbo, China
| | - Dun Wu
- Popsmart Technology (Zhejiang) Co., Ltd, Ningbo, China
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16
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Mishra V, Sharma MG. Digital transformation evaluation of telehealth using convergence, maturity, and adoption. HEALTH POLICY AND TECHNOLOGY 2022. [DOI: 10.1016/j.hlpt.2022.100684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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17
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Yoo HJ, Lee H. Critical role of information and communication technology in nursing during the COVID-19 pandemic: A qualitative study. J Nurs Manag 2022; 30:3677-3685. [PMID: 36325914 PMCID: PMC9877660 DOI: 10.1111/jonm.13880] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/26/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022]
Abstract
AIM To examine the need for information and communication technology (ICT)-based nursing care in improving patient management during the pandemic. BACKGROUND Maintaining traditional approaches to nursing in the ongoing coronavirus disease (COVID-19) pandemic predisposes health care systems to a risk of diminished quality of care. Using ICT (real-time videoconferencing, mobile robots and artificial intelligence) could reduce burnout and infection risks by minimizing face-to-face contact. METHOD Qualitative descriptive design with content analysis. RESULTS Overall, 24 participants (14 nurses, six medical/nursing informatics experts and four technology experts) were interviewed. Three main themes were extracted: emerging challenges for nurses due to COVID-19, impact of new technology on patient and nurse experiences and concerns with implementation of technology. CONCLUSION A significant portion of nurses' work was unrelated to professional nursing, causing burnout. ICT could help reduce nurses' burden by facilitating environmental management and non-contact communication and providing emotional support for patients. IMPLICATIONS FOR NURSING MANAGEMENT Establishing an ICT-based nursing care system that considers the physical environment and communication infrastructure of health care institutions, user's digital health literacy and user safety to effectively manage non-nursing care-related activities and undertake tasks that can be delegated may improve the quality of care for quarantined patients and reduce risk of cross-infection.
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Affiliation(s)
- Hye Jin Yoo
- College of NursingDankook UniversityCheonanSouth Korea
| | - Hyeongsuk Lee
- College of NursingGachon UniversityIncheonSouth Korea
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18
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Hate and False Metaphors: Implications to Emerging E-Participation Environment. FUTURE INTERNET 2022. [DOI: 10.3390/fi14110314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
This study aims to investigate the effect of metaphorical content on e-participation in healthcare. With this objective, the study assesses the awareness and capability of e-participants to navigate through healthcare metaphors during their participation. Healthcare-related e-participation data were collected from the Twitter platform. Data analysis includes (i) awareness measurements by topic modelling and sentiment analysis and (ii) participation abilities by problem-based learning models. Findings show that a lack of effort to validate metaphors harms e-participation levels and awareness, resulting in a problematic health environment. Exploring metaphors in these intricate forums has the potential to enhance service delivery. Improving web service delivery requires valuable input from stakeholders on the application of metaphors in the health domain.
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19
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An Improved Epidemiological Model for the Underprivileged People in the Contemporary Pandemics. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7890821. [PMID: 36267844 PMCID: PMC9578821 DOI: 10.1155/2022/7890821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/21/2022] [Indexed: 11/23/2022]
Abstract
In this work, we introduce an improved form of the basic SEIRD model based on Python simulation for the troublesome people who are oblivious about the contemporary pandemics due to diverse social impediments, especially those economically underprivileged. In the extant epidemiological models, some unorthodox issues are yet to be considered, such as poverty, illiteracy, and carelessness towards health issues, significantly influencing the data modeling. Our focus is to overcome these issues by adding two more branches, for instance, uncovered and apathetic people, which significantly influence the practical purposes. For the data simulation, we have used the Python-based algorithm that trains the desired system based on a set of real-time data with the proposed model and provides predicted data with a certain level of accuracy. Comparative discussions, statistical error analysis, and correlation-regression analysis have been introduced to validate the proposed epidemiological model. To show the numerical evidence, the investigation comprised the figurative and tabular modes for both real-time and predicted data. Finally, we discussed some concluding remarks based on our findings.
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20
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IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review. Healthcare (Basel) 2022; 10:healthcare10101993. [PMID: 36292441 PMCID: PMC9601552 DOI: 10.3390/healthcare10101993] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/30/2022] [Accepted: 10/06/2022] [Indexed: 11/04/2022] Open
Abstract
The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people’s lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems’ effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.
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21
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Elaziz MA, Ahmadein M, Ataya S, Alsaleh N, Forestiero A, Elsheikh AH. A Quantum-Based Chameleon Swarm for Feature Selection. MATHEMATICS 2022; 10:3606. [DOI: 10.3390/math10193606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
The Internet of Things is widely used, which results in the collection of enormous amounts of data with numerous redundant, irrelevant, and noisy features. In addition, many of these features need to be managed. Consequently, developing an effective feature selection (FS) strategy becomes a difficult goal. Many FS techniques, based on bioinspired metaheuristic methods, have been developed to tackle this problem. However, these methods still suffer from limitations; so, in this paper, we developed an alternative FS technique, based on integrating operators of the chameleon swarm algorithm (Cham) with the quantum-based optimization (QBO) technique. With the use of eighteen datasets from various real-world applications, we proposed that QCham is investigated and compared to well-known FS methods. The comparisons demonstrate the benefits of including a QBO operator in the Cham because the proposed QCham can efficiently and accurately detect the most crucial features. Whereas the QCham achieves nearly 92.6%, with CPU time(s) nearly 1.7 overall the tested datasets. This indicates the advantages of QCham among comparative algorithms and high efficiency of integrating the QBO with the operators of Cham algorithm that used to enhance the process of balancing between exploration and exploitation.
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22
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Jabbar MA, Shandilya SK, Kumar A, Shandilya S. Applications of cognitive internet of medical things in modern healthcare. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 102:108276. [PMID: 35958351 PMCID: PMC9356718 DOI: 10.1016/j.compeleceng.2022.108276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
The sudden outbreak of the novel coronavirus disease in 2019, known as COVID-19 has impacted the entire globe and has forced governments of various countries to a partial or full lockdown in the fear of the rapid spread of this disease. The major lesson learned from this pandemic is that there is a need to implement a robust system by using non-pharmaceutical interventions for the prevention and control of new contagious viruses. This goal can be achieved using the platform of the Internet of Things (IoT) because of its seamless connectivity and ubiquitous sensing ability. This technology-enabled healthcare sector is helpful to monitor COVID-19 patients properly by adopting an interconnected network. IoT is useful for improving patient satisfaction by reducing the rate of readmission in the hospital. The presented work discusses the applications and technologies of IoT like smart and wearable devices, drones, and robots which are used in healthcare systems to tackle the Coronavirus pandemic This paper focuses on applications of cognitive radio-based IoT for medical applications, which is referred to as "Cognitive Internet of Medical Things" (CIoMT). CIoMT is a disruptive and promising technology for dynamic monitoring, tracking, rapid diagnosis, and control of pandemics and to stop the spread of the virus. This paper explores the role of the CIoMT in the health domain, especially during pandemics, and also discusses the associated challenges and research directions.
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Affiliation(s)
- M A Jabbar
- Department of Computer Science, Vardhaman College of Engineering, Hyderabad, India
| | | | - Ajit Kumar
- Department of Computer Science, Soongsil University, South Korea
| | - Smita Shandilya
- Department of Electrical and Electronics, Sagar Institute of Research and Technology, India
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23
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Salem M, Elkaseer A, El-Maddah IAM, Youssef KY, Scholz SG, Mohamed HK. Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176625. [PMID: 36081081 PMCID: PMC9460364 DOI: 10.3390/s22176625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/22/2022] [Accepted: 08/30/2022] [Indexed: 05/06/2023]
Abstract
The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.
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Affiliation(s)
- Mahmoud Salem
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Correspondence: ; Tel.: +49-0-721-608-25632
| | - Ahmed Elkaseer
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Faculty of Engineering, Port Said University, Port Said 42526, Egypt
| | | | - Khaled Y. Youssef
- Faculty of Navigation Science and Space Technology, Beni-Suef University, Beni-Suef 2731070, Egypt
| | - Steffen G. Scholz
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- College of Engineering, Swansea University, Swansea SA2 8PP, UK
| | - Hoda K. Mohamed
- Faculty of Engineering, Ain Shams University, Cairo 11535, Egypt
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24
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Ong J, Tan G, Ang M, Chhablani J. Digital Advancements in Retinal Models of Care in the Post-COVID-19 Lockdown Era. Asia Pac J Ophthalmol (Phila) 2022; 11:403-407. [PMID: 36094383 DOI: 10.1097/apo.0000000000000533] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 03/14/2022] [Indexed: 11/25/2022] Open
Abstract
The coronavirus disease-2019 (COVID-19) pandemic introduced unique barriers to retinal care including limited access to imaging modalities, ophthalmic clinicians, and direct medical interventions. These unprecedented barriers were met with the robust implementation of digital advances to aid in monitoring and efficiency of retinal care while taking into the account of public safety. Many of these innovations have been successful in maintaining efficiency and patient satisfaction and are likely to stay to help preserve vision in the future. In this article we highlight these advances implemented during the pandemic including telescreening triage, virtual retinal imaging clinics, at-home optical coherence tomography, mobile phone self-monitoring, and virtual reality monitoring technology. We also discuss advancing innovations including Internet of Things and Blockchain technology that will be critical for further implementation and security of these digital advancements.
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Affiliation(s)
- Joshua Ong
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Gavin Tan
- Surgical Retinal Department of the Singapore National Eye Centre, Singapore
- Clinician Scientist, Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Marcus Ang
- Duke-NUS Department of Ophthalmology and Visual Sciences, Singapore
| | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
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25
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Abstract
Thanks to the proliferation of the Internet of Things (IoT), pervasive healthcare is gaining popularity day by day as it offers health support to patients irrespective of their location. In emergency medical situations, medical aid can be sent quickly. Though not yet standardized, this research direction, healthcare Internet of Things (H-IoT), attracts the attention of the research community, both academia and industry. In this article, we conduct a comprehensive survey of pervasive computing H-IoT. We would like to visit the wide range of applications. We provide a broad vision of key components, their roles, and connections in the big picture. We classify the vast amount of publications into different categories such as sensors, communication, artificial intelligence, infrastructure, and security. Intensively covering 118 research works, we survey (1) applications, (2) key components, their roles and connections, and (3) the challenges. Our survey also discusses the potential solutions to overcome the challenges in this research field.
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26
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Subramanian M, Shanmuga Vadivel K, Hatamleh WA, Alnuaim AA, Abdelhady M, V E S. The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis. EXPERT SYSTEMS 2022; 39:e12834. [PMID: 34898797 PMCID: PMC8646626 DOI: 10.1111/exsy.12834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/10/2021] [Accepted: 09/09/2021] [Indexed: 05/17/2023]
Abstract
Following the COVID-19 pandemic, there has been an increase in interest in using digital resources to contain pandemics. To avoid, detect, monitor, regulate, track, and manage diseases, predict outbreaks and conduct data analysis and decision-making processes, a variety of digital technologies are used, ranging from artificial intelligence (AI)-powered machine learning (ML) or deep learning (DL) focused applications to blockchain technology and big data analytics enabled by cloud computing and the internet of things (IoT). In this paper, we look at how emerging technologies such as the IoT and sensors, AI, ML, DL, blockchain, augmented reality, virtual reality, cloud computing, big data, robots and drones, intelligent mobile apps, and 5G are advancing health care and paving the way to combat the COVID-19 pandemic. The aim of this research is to look at possible technologies, processes, and tools for addressing COVID-19 issues such as pre-screening, early detection, monitoring infected/quarantined individuals, forecasting future infection rates, and more. We also look at the research possibilities that have arisen as a result of the use of emerging technology to handle the COVID-19 crisis.
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Affiliation(s)
- Malliga Subramanian
- Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India
| | | | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia
| | - Abeer Ali Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community Services King Saud University Riyadh Saudi Arabia
| | - Mohamed Abdelhady
- Electrical and Computer Engineering Department Cleveland State University Cleveland Ohio USA
| | - Sathishkumar V E
- Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India
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27
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Ramírez-del Real T, Martínez-García M, Márquez MF, López-Trejo L, Gutiérrez-Esparza G, Hernández-Lemus E. Individual Factors Associated With COVID-19 Infection: A Machine Learning Study. Front Public Health 2022; 10:912099. [PMID: 35844896 PMCID: PMC9279686 DOI: 10.3389/fpubh.2022.912099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/24/2022] [Indexed: 11/13/2022] Open
Abstract
The fast, exponential increase of COVID-19 infections and their catastrophic effects on patients' health have required the development of tools that support health systems in the quick and efficient diagnosis and prognosis of this disease. In this context, the present study aims to identify the potential factors associated with COVID-19 infections, applying machine learning techniques, particularly random forest, chi-squared, xgboost, and rpart for feature selection; ROSE and SMOTE were used as resampling methods due to the existence of class imbalance. Similarly, machine and deep learning algorithms such as support vector machines, C4.5, random forest, rpart, and deep neural networks were explored during the train/test phase to select the best prediction model. The dataset used in this study contains clinical data, anthropometric measurements, and other health parameters related to smoking habits, alcohol consumption, quality of sleep, physical activity, and health status during confinement due to the pandemic associated with COVID-19. The results showed that the XGBoost model got the best features associated with COVID-19 infection, and random forest approximated the best predictive model with a balanced accuracy of 90.41% using SMOTE as a resampling technique. The model with the best performance provides a tool to help prevent contracting SARS-CoV-2 since the variables with the highest risk factor are detected, and some of them are, to a certain extent controllable.
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Affiliation(s)
- Tania Ramírez-del Real
- Cátedras Conacyt, National Council on Science and Technology, Mexico City, Mexico
- Center for Research in Geospatial Information Sciences, Mexico City, Mexico
| | - Mireya Martínez-García
- Clinical Research Division, National Institute of Cardiology “Ignacio Chávez”, Mexico City, Mexico
| | - Manlio F. Márquez
- Clinical Research Division, National Institute of Cardiology “Ignacio Chávez”, Mexico City, Mexico
| | - Laura López-Trejo
- Institute for Security and Social Services of State Workers, Mexico City, Mexico
| | - Guadalupe Gutiérrez-Esparza
- Cátedras Conacyt, National Council on Science and Technology, Mexico City, Mexico
- Clinical Research Division, National Institute of Cardiology “Ignacio Chávez”, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
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Kumar P, Singh RK, Shahgholian A. Learnings from COVID-19 for managing humanitarian supply chains: systematic literature review and future research directions. ANNALS OF OPERATIONS RESEARCH 2022:1-37. [PMID: 35694371 PMCID: PMC9175170 DOI: 10.1007/s10479-022-04753-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/29/2022] [Indexed: 05/03/2023]
Abstract
The COVID-19 pandemic has been experienced as the most significant global disaster after the Spanish flue in 1918. Millions of people lost their life due to a lack of preparedness and ineffective strategies for managing humanitarian supply chains (HSC). Based on the learnings from this pandemic outbreak, different strategies for managing the effective HSC have been explored in the present context of pandemics through a systematic literature review. The findings highlight some of the major challenges faced during the COVID-19 pandemic, such as lack of planning and preparedness, extended shortages of essential lifesaving items, inadequate lab capacity, lack of transparency and visibility, inefficient distribution network, high response time, dependencies on single sourcing for the medical equipment and medicines, lack of the right information on time, and lack of awareness about the protocol for the treatment of the viral disease. Some of the significant learnings observed from this analysis are the use of multiple sourcing of essential items, joint procurement, improving collaboration among all stakeholders, applications of IoT and blockchain technologies for improving tracking and traceability of essential commodities, application of data analytics tools for accurate prediction of next possible COVID wave/disruptions and optimization of distribution network. Limited studies are focused on finding solutions to these problems in managing HSC. Therefore, as a future scope, researchers could find solutions to optimizing the distribution network in context to pandemics, improving tracing and tracking of items during sudden demand, improving trust and collaborations among different agencies involved in HSC.
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Affiliation(s)
- Pravin Kumar
- Department of Mechanical Engineering, Delhi Technological University, Delhi, India
| | | | - Azar Shahgholian
- Liverpool Business School, Liverpool John Moores University, Liverpool, UK
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Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review. APPLIED SYSTEM INNOVATION 2022. [DOI: 10.3390/asi5030051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). “Smart-Home” was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories—Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies—were developed and discussed. There is an imperative need to monitor CVD patients’ vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients’ needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency’s role to be followed when designing, developing, and deploying Smart Home Technology for CVD.
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IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7713939. [PMID: 35432824 PMCID: PMC9006083 DOI: 10.1155/2022/7713939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/12/2022] [Accepted: 03/14/2022] [Indexed: 01/08/2023]
Abstract
COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.
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Sony M, Antony J, McDermott O. The impact of medical cyber–physical systems on healthcare service delivery. TQM JOURNAL 2022. [DOI: 10.1108/tqm-01-2022-0005] [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
PurposeThe pandemic has reinforced the need for revamping the healthcare service delivery systems around the world to meet the increased challenges of modern-day illnesses. The use of medical cyber–physical system (MCPS) in the healthcare is one of the means of transforming the landscape of the traditional healthcare service delivery system. The purpose of this study is to critically examine the impact of MCPS on the quality of healthcare service delivery.Design/methodology/approachThis paper uses an evidence-based approach, the authors have conducted a systematic literature review to study the impact of MCPS on healthcare service delivery. Fifty-four articles were thematically examined to study the impact of MCPS on eight characteristics of the healthcare service delivery proposed by the world health organisation.FindingsThe study proposes support that MCPS will positively impact (1) comprehensiveness, (2) accessibility, (3) coverage, (4) continuity, (5) quality, (6) person-centredness, (7) coordination, (8) accountability and (9) efficiency dimension of the healthcare service delivery. The study further draws nine propositions to support the impact of MCPS on the healthcare service delivery.Practical implicationsThis study can be used by stakeholders as a guide point while using MCPS in healthcare service delivery systems. Besides, healthcare managers can use this study to understand the performance of their healthcare system. This study can further be used for designing effective strategies for deploying MCPS to be effective and efficient in each of the dimensions of healthcare service delivery.Originality/valueThe previous studies have focussed on technology aspects of MCPS and none of them critically analysed the impact on healthcare service delivery. This is the first literature review carried out to understand the impact of MCPS on the nine dimensions of healthcare service delivery proposed by WHO. This study provides improved thematic awareness of the resulting body of knowledge, allowing the field of MCPS and healthcare service delivery to progress in a more informed and multidisciplinary manner.
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Forensic Analysis on Internet of Things (IoT) Device Using Machine-to-Machine (M2M) Framework. ELECTRONICS 2022. [DOI: 10.3390/electronics11071126] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The versatility of IoT devices increases the probability of continuous attacks on them. The low processing power and low memory of IoT devices have made it difficult for security analysts to keep records of various attacks performed on these devices during forensic analysis. The forensic analysis estimates how much damage has been done to the devices due to various attacks. In this paper, we have proposed an intelligent forensic analysis mechanism that automatically detects the attack performed on IoT devices using a machine-to-machine (M2M) framework. Further, the M2M framework has been developed using different forensic analysis tools and machine learning to detect the type of attacks. Additionally, the problem of an evidence acquisition (attack on IoT devices) has been resolved by introducing a third-party logging server. Forensic analysis is also performed on logs using forensic server (security onion) to determine the effect and nature of the attacks. The proposed framework incorporates different machine learning (ML) algorithms for the automatic detection of attacks. The performance of these models is measured in terms of accuracy, precision, recall, and F1 score. The results indicate that the decision tree algorithm shows the optimum performance as compared to the other algorithms. Moreover, comprehensive performance analysis and results presented validate the proposed model.
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Mahmoudi T, Naghdi T, Morales-Narváez E, Golmohammadi H. Toward smart diagnosis of pandemic infectious diseases using wastewater-based epidemiology. Trends Analyt Chem 2022; 153:116635. [PMID: 35440833 PMCID: PMC9010328 DOI: 10.1016/j.trac.2022.116635] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 03/21/2022] [Accepted: 04/07/2022] [Indexed: 12/12/2022]
Abstract
COVID-19 outbreak revealed fundamental weaknesses of current diagnostic systems, particularly in prediction and subsequently prevention of pandemic infectious diseases (PIDs). Among PIDs detection methods, wastewater-based epidemiology (WBE) has been demonstrated to be a favorable mean for estimation of community-wide health. Besides, by going beyond purely sensing usages of WBE, it can be efficiently exploited in Healthcare 4.0/5.0 for surveillance, monitoring, control, and above all prediction and prevention, thereby, resulting in smart sensing and management of potential outbreaks/epidemics/pandemics. Herein, an overview of WBE sensors for PIDs is presented. The philosophy behind the smart diagnosis of PIDs using WBE with the help of digital technologies is then discussed, as well as their characteristics to be met. Analytical techniques that are pushing the frontiers of smart sensing and have a high potential to be used in the smart diagnosis of PIDs via WBE are surveyed. In this context, we underscore key challenges ahead and provide recommendations for implementing and moving faster toward smart diagnostics.
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Premalatha G, Bai VT. Wireless IoT and Cyber-Physical System for Health Monitoring Using Honey Badger Optimized Least-Squares Support-Vector Machine. WIRELESS PERSONAL COMMUNICATIONS 2022; 124:3013-3034. [PMID: 35370364 PMCID: PMC8963677 DOI: 10.1007/s11277-022-09500-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/06/2022] [Indexed: 06/14/2023]
Abstract
Health monitoring is a prominent factor in a person's daily life. Healthcare for the elderly is becoming increasingly important as the population ages and grows. The health of an Elderly patient needs frequent examination because the health deteriorates with an increasing age profile. IoT is utilized everywhere in the health industry to identify and communicate with the patients by the professional. A cyber-physical system (CPS) is used to combine physical processes with communication and computation. CPS and IoT are both wirelessly connected via information and communication technologies. The novelty of the research lies in the Honey Badger (HB) algorithm optimized Least-squares Support-Vector Machine (LS-SVM) architecture proposed in this paper for monitoring multi parameters to categorize and determine the abnormal patient details present in the dataset. Since the performance of the LS-SVM is highly dependent on the width coefficient and regularization factor, the HB algorithm is employed in this study to optimize both parameters. The HB algorithm is capable of solving the medical problem that has a complex search space and it also improves the convergence performance of the LS-SVM classifier by achieving a tradeoff between the exploration and exploitation phases. The HB optimized LS-SVM classifier predicts the patients with deteriorating health conditions and evaluates the accuracy of the results obtained. In the end, the statistical data is provided to the caretaker via a smartphone application as a monthly statistical report. The proposed model offers a Positive Predictive Value (PPV), Negative Predictive Value (NPV), and an Area Under the Curve (AUC) score of 0.9478, 0.9587, and 0.9617 respectively which is relatively higher than the conventional techniques such as Decision tree, Random Forest, and Support Vector Machine (SVM) classifier. The simulation results demonstrate that the proposed model efficiently models the sensor parameters and offers timely support to elderly patients.
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Affiliation(s)
- G. Premalatha
- Prathyusha Engineering College, Thiruvallur, Chennai, India
- KCG College of Technology, Chennai, India
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35
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Sony M, Antony J, McDermott O. The Impact of Healthcare 4.0 on the Healthcare Service Quality: A Systematic Literature Review. Hosp Top 2022; 101:288-304. [PMID: 35324390 DOI: 10.1080/00185868.2022.2048220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Healthcare 4.0 is inspired by Industry 4.0 and its application has resulted in a paradigmatic shift in the field of healthcare. However, the impact of this digital revolution in the healthcare system on healthcare service quality is not known. The purpose of this study is to examine the impact of healthcare 4.0 on healthcare service quality. This study used the systematic literature review methodology suggested by Transfield et al. to critically examine 67 articles. The impact of healthcare 4.0 is analyzed in-depth in terms of the interpersonal, technical, environmental, and administrative aspect of healthcare service quality. This study will be useful to hospitals and other stakeholders to understand the impact of healthcare 4.0 on the service quality of health systems. Besides, this study critically analyses the existing literature and identifies research areas in this field and hence will be beneficial to researchers. Though there are few literature reviews in healthcare 4.0, this is the first study to examine the impact of Healthcare 4.0 on healthcare service quality.
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Affiliation(s)
- Michael Sony
- WITS Business School, University of Witwatersrand, Johannesburg, South Africa
| | - Jiju Antony
- Industrial and Systems Engineering, Khalifa University, Abu Dhabi, UAE
| | - Olivia McDermott
- College of Engineering and Science, National University of Ireland, Gallway, Ireland
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Shamsabadi A, Pashaei Z, Karimi A, Mirzapour P, Qaderi K, Marhamati M, Barzegary A, Fakhfouri A, Mehraeen E, SeyedAlinaghi S, Dadras O. Internet of things in the management of chronic diseases during the COVID‐19 pandemic: A systematic review. Health Sci Rep 2022; 5:e557. [PMID: 35308419 PMCID: PMC8919365 DOI: 10.1002/hsr2.557] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/13/2022] [Accepted: 02/20/2022] [Indexed: 02/05/2023] Open
Abstract
Introduction Methods Results Conclusion
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Affiliation(s)
- Ahmadreza Shamsabadi
- Department of Health Information Technology Esfarayen Faculty of Medical Sciences Esfarayen Iran
| | - Zahra Pashaei
- Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High‐Risk Behaviors Tehran University of Medical Sciences Tehran Iran
| | - Amirali Karimi
- School of Medicine Tehran University of Medical Sciences Tehran Iran
| | - Pegah Mirzapour
- Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High‐Risk Behaviors Tehran University of Medical Sciences Tehran Iran
| | - Kowsar Qaderi
- Department of Midwifery Kermanshah University of Medical Sciences Kermanshah Iran
| | - Mahmoud Marhamati
- Instructor of Medical Surgical Nursing, Department of Nursing Esfarayen Faculty of Medical Sciences Esfarayen Iran
| | | | | | - Esmaeil Mehraeen
- Department of Health Information Technology Khalkhal University of Medical Sciences Khalkhal Iran
| | - SeyedAhmad SeyedAlinaghi
- Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High‐Risk Behaviors Tehran University of Medical Sciences Tehran Iran
| | - Omid Dadras
- School of Public Health Walailak University Thai Buri Nakhon Si Thammarat Thailand
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Open Innovation in Times of Crisis: An Overview of the Healthcare Sector in Response to the COVID-19 Pandemic. JOURNAL OF OPEN INNOVATION: TECHNOLOGY, MARKET, AND COMPLEXITY 2022; 8. [PMCID: PMC9906727 DOI: 10.3390/joitmc8010021] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The COVID-19 pandemic has caused huge and disruptive technological changes in the healthcare sector, transforming the way businesses and societies function. To respond to the global health crisis, there have been numerous innovation projects in the healthcare sector, including the fast design and manufacturing of personal protective equipment (PPE) and medical devices, and testing, treatment, and vaccine technologies. Many of these innovative activities happen beyond organizational boundaries with collaboration and open innovation. In this paper, we review the current literature on open innovation strategy during the pandemic and adopt the co-evolution view of business ecosystems to address the context of change. Based on a detailed exploration of the COVID-19-related technologies in the UK and global healthcare sectors, we identify the key emerging themes of open innovation in crisis. Further discussions are conducted in relation to each theme. Our results and analysis can help provide policy recommendations for the healthcare sector, businesses, and society to recover from the crisis.
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Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data. SENSORS 2022; 22:s22031205. [PMID: 35161951 PMCID: PMC8838838 DOI: 10.3390/s22031205] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/22/2022] [Accepted: 01/24/2022] [Indexed: 12/14/2022]
Abstract
Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient’s health condition remotely. Every layer has specific functionality in the COVID-19 symptoms’ monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.
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BOSTANCI S, YILDIRIM S, ERDOĞAN F. A review on e-Government Portal’s services within Hospital Information System during Covid-19 pandemic. KONURALP TIP DERGISI 2022. [DOI: 10.18521/ktd.1036010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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Sultana N, Tamanna M. Evaluating the Potential and Challenges of IoT in Education and Other Sectors during the COVID-19 Pandemic: The Case of Bangladesh. TECHNOLOGY IN SOCIETY 2022; 68:101857. [PMID: 35043024 PMCID: PMC8758399 DOI: 10.1016/j.techsoc.2021.101857] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 12/20/2021] [Accepted: 12/30/2021] [Indexed: 06/14/2023]
Abstract
The Internet of Things (IoT) adoption affects different sectors immensely, especially during Covid-19. This study mainly examines the benefits and challenges experienced in Bangladesh's education, and corporate and service sectors while using IoT services during COVID-19. Data collection was performed using a convenient random sampling method and distributing questions online. Two hundred sixty completed responses were analyzed, where 40% of responses were from the education sector, and 60% were from the corporate and service sector. The research method was quantitative and empirical. The study reveals that people find saving time the most potential in education sector, whereas, in the corporate and service sector, the topmost benefit of using IoT services is that it helps strictly maintain physical distance. Conversely, the most significant challenges people face in both sectors are that the IoT increases social distance and reduces individual communication. Nevertheless, people in both sectors have a positive attitude towards using IoT in the future. The findings have practical implications for business professionals, academic scholars, and other associated parties keen to identify IoT impact during the pandemic.
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Affiliation(s)
- Nahida Sultana
- Department of Business Administration (MIS), Bangladesh University, Bangladesh
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Saeed U, Shah SY, Ahmad J, Imran MA, Abbasi QH, Shah SA. Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review. J Pharm Anal 2022; 12:193-204. [PMID: 35003825 PMCID: PMC8724017 DOI: 10.1016/j.jpha.2021.12.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 12/29/2021] [Accepted: 12/30/2021] [Indexed: 12/20/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people worldwide. With the recent rise of new Delta and Omicron variants, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, radar, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions. This article describes cutting-edge technology (invasive/non-invasive) and its role in the recognition of COVID-19 symptoms. This article summarizes state-of-art machine-learning algorithms and their roles in modern healthcare systems. This article presents the challenges associated with wireless sensing techniques and potential future research directions.
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Affiliation(s)
- Umer Saeed
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, UK
| | - Syed Yaseen Shah
- School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, G4 0BA, UK
| | - Jawad Ahmad
- School of Computing, Edinburgh Napier University, Edinburgh, EH11 4BN, UK
| | - Muhammad Ali Imran
- James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Qammer H Abbasi
- James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Syed Aziz Shah
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, UK
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Mbunge E, Jiyane S, Muchemwa B. Towards emotive sensory Web in virtual health care: Trends, technologies, challenges and ethical issues. SENSORS INTERNATIONAL 2022. [DOI: 10.1016/j.sintl.2021.100134] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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43
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Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
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Gamberini L, Pluchino P, Bacchin D, Zanella A, Orso V, Anna S, Mapelli D. IoT as Non-Pharmaceutical Interventions for the Safety of Living Environments in COVID-19 Pandemic Age. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.733645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The outbreak of the Sars-Cov-2 pandemic has changed our perception of safety in shared and public living environments including healthcare facilities, shops, schools, and enterprises. The Internet of Things (IoT) represents a suitable solution for managing anti-pandemic smart devices (e.g., UV lights, smart cameras, etc.) and increasing citizens’ safety in public health crises. In this paper, we highlighted how IoT technologies can be exploited as non-pharmaceutical interventions presenting the SAFE PLACE project as an implementation of this concept. The project meant to design and develop an IoT system to ensure the safety and salubrity of shared environments. Advanced algorithms will be exploited to detect and classify humans’ presence, gathering, usage of personal protective equipment, and considering carefully the privacy protection of individuals.
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Sultana N, Tamanna M. Exploring the benefits and challenges of Internet of Things (IoT) during Covid-19: a case study of Bangladesh. DISCOVER INTERNET OF THINGS 2021. [PMCID: PMC8601514 DOI: 10.1007/s43926-021-00020-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The Internet of Things (IoT) is expected to have a huge impact, especially during the pandemic period. The study reveals that people are using the IoT mostly for education purposes (as students and educators), office work, banks and medical purposes during the pandemic. The topmost benefit of using IoT services experienced by people during pandemic situations is that it helps to strictly maintain physical distance. However, the greatest challenge faced by people is that the use of the IoT increases social distancing and reduces personal communication. Data were collected through a questionnaire distributed online and using a convenient random sampling method. A total of 260 participants’ properly completed responses were analyzed after conducting Three-fold validation. Research method was quantitative and empirical. Although, some studies have been found about IoT prospects in Bangladesh, no study has specifically explored the benefits and challenges of IoT services in diverse fields of Bangladesh during this new normal COVID-19 situation. The results can be beneficial to academic scholars, business professionals and organizations in different sectors and any other parties interested in determining the impact of IoT services on pandemic.
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Affiliation(s)
- Nahida Sultana
- Department of MIS, Bangladesh University, Dhaka, 1207 Bangladesh
| | - Marzia Tamanna
- Department of MIS, East West University, Dhaka, 1212 Bangladesh
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Zhang M, Dai D, Hou S, Liu W, Gao F, Xu D, Hu Y. Thinking on the informatization development of China's healthcare system in the post-COVID-19 era. ACTA ACUST UNITED AC 2021; 1:24-28. [PMID: 34777904 PMCID: PMC8571484 DOI: 10.1016/j.imed.2021.03.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 11/30/2022]
Abstract
With the application of Internet of Things, big data, cloud computing, artificial intelligence, and other cutting-edge technologies, China's medical informatization is developing rapidly. In this paper, we summaried the role of information technology in healthcare sector's battle against the Coronavirus disease 2019 (COVID-19) from the perspectives of early warning and monitoring, screening and diagnosis, medical treatment and scientific research, analyzes the bottlenecks of the development of information technology in the post-COVID-19 era, and puts forward feasible suggestions for further promoting the construction of medical informatization from the perspectives of sharing, convenience, and safety.
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Affiliation(s)
- Ming Zhang
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Danyun Dai
- International Exchange Office, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Siliang Hou
- International Exchange Office, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Wei Liu
- Computer Management Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Feng Gao
- International Exchange Office, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Dong Xu
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Yu Hu
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
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Rahman A, Rahman M, Kundu D, Karim MR, Band SS, Sookhak M. Study on IoT for SARS-CoV-2 with healthcare: present and future perspective. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:9697-9726. [PMID: 34814364 DOI: 10.3934/mbe.2021475] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The ever-evolving and contagious nature of the Coronavirus (COVID-19) has immobilized the world around us. As the daily number of infected cases increases, the containment of the spread of this virus is proving to be an overwhelming task. Healthcare facilities around the world are overburdened with an ominous responsibility to combat an ever-worsening scenario. To aid the healthcare system, Internet of Things (IoT) technology provides a better solution-tracing, testing of COVID patients efficiently is gaining rapid pace. This study discusses the role of IoT technology in healthcare during the SARS-CoV-2 pandemics. The study overviews different research, platforms, services, products where IoT is used to combat the COVID-19 pandemic. Further, we intelligently integrate IoT and healthcare for COVID-19 related applications. Again, we focus on a wide range of IoT applications in regards to SARS-CoV-2 tracing, testing, and treatment. Finally, we effectively consider further challenges, issues, and some direction regarding IoT in order to uplift the healthcare system during COVID-19 and future pandemics.
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Affiliation(s)
- Anichur Rahman
- Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of Dhaka University, Savar, Dhaka-1350, Bangladesh
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Muaz Rahman
- Department of Electrical and Electronic Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of Dhaka University, Savar, Dhaka-1350, Bangladesh
| | - Dipanjali Kundu
- Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of Dhaka University, Savar, Dhaka-1350, Bangladesh
| | - Md Razaul Karim
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
| | - Mehdi Sookhak
- Dept. of Computer Science, Texas A & M University-Corpus Christi, 6300 Ocean Drive, Corpus Christi, Texas, USA, 78412
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Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19. ELECTRONICS 2021. [DOI: 10.3390/electronics10151834] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
From the end of 2019, the world has been facing the threat of COVID-19. It is predicted that, before herd immunity is achieved globally via vaccination, people around the world will have to tackle the COVID-19 pandemic using precautionary steps. This paper suggests a COVID-19 identification and control system that operates in real-time. The proposed system utilizes the Internet of Things (IoT) platform to capture users’ time-sensitive symptom information to detect potential cases of coronaviruses early on, to track the clinical measures adopted by survivors, and to gather and examine appropriate data to verify the existence of the virus. There are five key components in the framework: symptom data collection and uploading (via communication technology), a quarantine/isolation center, an information processing core (using artificial intelligent techniques), cloud computing, and visualization to healthcare doctors. This research utilizes eight machine/deep learning techniques—Neural Network, Decision Table, Support Vector Machine (SVM), Naive Bayes, OneR, K-Nearest Neighbor (K-NN), Dense Neural Network (DNN), and the Long Short-Term Memory technique—to detect coronavirus cases from time-sensitive information. A simulation was performed to verify the eight algorithms, after selecting the relevant symptoms, on real-world COVID-19 data values. The results showed that five of these eight algorithms obtained an accuracy of over 90%. Conclusively, it is shown that real-world symptomatic information would enable these three algorithms to identify potential COVID-19 cases effectively with enhanced accuracy. Additionally, the framework presents responses to treatment for COVID-19 patients.
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IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2021.06.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
The COVID-19 pandemic provided a much-needed sanity check for IoT-inspired frameworks and solutions. IoT solutions such as remote health monitoring and contact tracing provided support for authorities to successfully manage the spread of the coronavirus. This article provides the first comprehensive review of key IoT solutions that have had an impact on COVID-19 in healthcare, contact tracing, and transportation during the pandemic. Each sector is investigated in depth; and potential applications, social and economic impact, and barriers for mass adaptation are discussed in detail. Furthermore, it elaborates on the challenges and opportunities for IoT framework solutions in the immediate post-COVID-19 era. To this end, privacy and security concerns of IoT applications are analyzed in depth and emerging standards and code of practices for mass adaptation are also discussed. The main contribution of this review paper is the in-depth analysis and categorization of sector-wise IoT technologies, which have the potential to be prominent applications in the new normal. IoT applications in each selected sector are rated for their potential economic and social impact, timeline for mass adaptation, and Technology Readiness Level (TRL). In addition, this article outlines potential research directions for next-generation IoT applications that would facilitate improved performance with preserved privacy and security, as well as wider adaptation by the population at large.
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