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Oudbier SJ, Souget-Ruff SP, Chen BSJ, Ziesemer KA, Meij HJ, Smets EMA. Implementation barriers and facilitators of remote monitoring, remote consultation and digital care platforms through the eyes of healthcare professionals: a review of reviews. BMJ Open 2024; 14:e075833. [PMID: 38858155 PMCID: PMC11168143 DOI: 10.1136/bmjopen-2023-075833] [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: 05/19/2023] [Accepted: 05/14/2024] [Indexed: 06/12/2024] Open
Abstract
OBJECTIVES Digital transformation in healthcare is a necessity considering the steady increase in healthcare costs, the growing ageing population and rising number of people living with chronic diseases. The implementation of digital health technologies in patient care is a potential solution to these issues, however, some challenges remain. In order to navigate such complexities, the perceptions of healthcare professionals (HCPs) must be considered. The objective of this umbrella review is to identify key barriers and facilitators involved in digital health technology implementation, from the perspective of HCPs. DESIGN Systematic umbrella review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. DATA SOURCES Embase.com, PubMed and Web of Science Core Collection were searched for existing reviews dated up to 17 June 2022. Search terms included digital health technology, combined with terms related to implementation, and variations in terms encompassing HCP, such as physician, doctor and the medical discipline. ELIGIBILITY CRITERIA Quantitative and qualitative reviews evaluating digital technologies that included patient interaction were considered eligible. Three reviewers independently synthesised and assessed eligible reviews and conducted a critical appraisal. DATA EXTRACTION AND SYNTHESIS Regarding the data collection, two reviewers independently synthesised and interpreted data on barriers and facilitators. RESULTS Thirty-three reviews met the inclusion criteria. Barriers and facilitators were categorised into four levels: (1) the organisation, (2) the HCP, (3) the patient and (4) technical aspects. The main barriers and facilitators identified were (lack of) training (n=22/33), (un)familiarity with technology (n=17/33), (loss of) communication (n=13/33) and security and confidentiality issues (n=17/33). Barriers of key importance included increased workload (n=16/33), the technology undermining aspects of professional identity (n=11/33), HCP uncertainty about patients' aptitude with the technology (n=9/33), and technical issues (n=12/33). CONCLUSIONS The implementation strategy should address the key barriers highlighted by HCPs, for instance, by providing adequate training to familiarise HCPs with the technology, adapting the technology to the patient preferences and addressing technical issues. Barriers on both HCP and patient levels can be overcome by investigating the needs of the end-users. As we shift from traditional face-to-face care models towards new modes of care delivery, further research is needed to better understand the role of digital technology in the HCP-patient relationship.
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Affiliation(s)
- Susan J Oudbier
- Outpatient Division, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Medical Psychology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
- Amsterdam Public Health research institute, Digital Health, Amsterdam, The Netherlands
- Amsterdam Public Health research institute, Quality of Care, Amsterdam, The Netherlands
| | - Sylvie P Souget-Ruff
- Department of Medical Psychology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Britney S J Chen
- Department of Medical Psychology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Kirsten A Ziesemer
- Medical Library, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Hans J Meij
- Outpatient Division, Amsterdam UMC, Amsterdam, The Netherlands
- National University of Singapore Yong Loo Lin School of Medicine, Singapore
| | - Ellen M A Smets
- Department of Medical Psychology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
- Amsterdam Public Health research institute, Quality of Care, Amsterdam, The Netherlands
- Amsterdam Public Health research institute, Personalized Medicine, Amsterdam, The Netherlands
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Chan HF, Cheng Z, Mendolia S, Paloyo AR, Tani M, Proulx D, Savage DA, Torgler B. Residential mobility restrictions and adverse mental health outcomes during the COVID-19 pandemic in the UK. Sci Rep 2024; 14:1790. [PMID: 38245576 PMCID: PMC10799952 DOI: 10.1038/s41598-024-51854-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
During the COVID-19 pandemic, several governments tried to contain the spread of SARS-CoV-2, the virus that causes COVID-19, with lockdowns that prohibited leaving one's residence unless carrying out a few essential services. We investigate the relationship between limitations to mobility and mental health in the UK during the first year and a half of the pandemic using a unique combination of high-frequency mobility data from Google and monthly longitudinal data collected through the Understanding Society survey. We find a strong and statistically robust correlation between mobility data and mental health survey data and show that increased residential stationarity is associated with the deterioration of mental wellbeing even when regional COVID-19 prevalence and lockdown stringency are controlled for. The relationship is heterogeneous, as higher levels of distress are seen in young, healthy people living alone; and in women, especially if they have young children.
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Affiliation(s)
- Ho Fai Chan
- School of Economics and Finance, Queensland University of Technology, Brisbane, QLD, 4000, Australia.
- Centre for Behavioural Economics, Society and Technology (BEST), Brisbane, QLD, 4000, Australia.
- Centre for Behavioural Insights for Technology Adoption (BITA), Brisbane, QLD, 4000, Australia.
| | - Zhiming Cheng
- Social Policy Research Centre, University of New South Wales, Kensington, NSW, 2052, Australia
- Department of Management, Macquarie Business School, Macquarie University, Sydney, NSW, 2109, Australia
| | - Silvia Mendolia
- Department of Economics, Social Studies and Applied Mathematics and Statistics, University of Turin, Turin, Italy
| | | | | | - Damon Proulx
- Newcastle Business School, University of Newcastle, Newcastle, NSW, Australia
| | - David A Savage
- Newcastle Business School, University of Newcastle, Newcastle, NSW, Australia
| | - Benno Torgler
- School of Economics and Finance, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- Centre for Behavioural Economics, Society and Technology (BEST), Brisbane, QLD, 4000, Australia
- Centre for Behavioural Insights for Technology Adoption (BITA), Brisbane, QLD, 4000, Australia
- Newcastle Business School, University of Newcastle, Newcastle, NSW, Australia
- CREMA - Center for Research in Economics, Management and the Arts, Basel, Switzerland
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Chopade SS, Gupta HP, Dutta T. Survey on Sensors and Smart Devices for IoT Enabled Intelligent Healthcare System. WIRELESS PERSONAL COMMUNICATIONS 2023; 131:1-39. [PMID: 37360143 PMCID: PMC10258751 DOI: 10.1007/s11277-023-10528-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 06/28/2023]
Abstract
The Internet of Things (IoT) in the healthcare system is rapidly changing from the conventional hospital and concentrated specialist behavior to a distributed, patient-centric approach. With the advancement of new techniques, a patient needs sophisticated healthcare requirements. IoT-enabled intelligent health monitoring system with sensors and devices is a patient analysis technique to monitor the patient 24 h a day. IoT is swapping the architecture and has improved the application of different complex systems. Healthcare devices are one of the most remarkable applications of the IoT. Many patient monitoring techniques are available in the IoT platform. This review presents an IoT-enabled intelligent health monitoring system by analyzing the papers reported between 2016 and 2023. This survey also discusses the concept of big data in IoT networks and the IoT computing technology known as edge computing. This review concentrated on sensors and smart devices used in intelligent IoT based health monitoring systems with merits and demerits. This survey gives a brief study based on sensors and smart devices used in IoT smart healthcare systems.
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Affiliation(s)
- Swati Sandeep Chopade
- Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005 India
| | - Hari Prabhat Gupta
- Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005 India
| | - Tanima Dutta
- Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005 India
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4
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Khan S, Khan HU, Nazir S. Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing. Sci Rep 2022; 12:22377. [PMID: 36572709 PMCID: PMC9792582 DOI: 10.1038/s41598-022-26090-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/09/2022] [Indexed: 12/27/2022] Open
Abstract
Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.
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Affiliation(s)
- Sulaiman Khan
- grid.412603.20000 0004 0634 1084Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
| | - Habib Ullah Khan
- grid.412603.20000 0004 0634 1084Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
| | - Shah Nazir
- grid.502337.00000 0004 4657 4747Department of Computer Science, University of Swabi, Swabi, Pakistan
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Praveen SP, Murali Krishna TB, Anuradha CH, Mandalapu SR, Sarala P, Sindhura S. A robust framework for handling health care information based on machine learning and big data engineering techniques. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2157071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- S. Phani Praveen
- Department of Computer Science & Engineering, PVP Siddhartha Institute of Technology, Vijayawada, India
| | - T. Bala Murali Krishna
- Department of Computer Science & Engineering, Dhanekula Institute of Engineering & Technology, Vijayawada, India
| | - C. H. Anuradha
- Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada, India
| | - Srinivasa Rao Mandalapu
- Department of Computer Science & Engineering, RVR & JC College of Engineering, Guntur, India
| | - Pappula Sarala
- Department of Computer Science & Engineering, Lakireddy Balireddy College of Engineering, Mylavaram, India
| | - S. Sindhura
- Department of Computer Science & Engineering, NRI Institute of Technology, Vijayawada, India
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Saikumar K, Rajesh V, Srivastava G, Lin JCW. Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network. Front Comput Neurosci 2022; 16:964686. [PMID: 36277609 PMCID: PMC9585537 DOI: 10.3389/fncom.2022.964686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 09/09/2022] [Indexed: 11/15/2022] Open
Abstract
Heart disease is an emerging health issue in the medical field, according to WHO every year around 10 billion people are affected with heart abnormalities. Arteries in the heart generate oxygenated blood to all body parts, however sometimes blood vessels become clogged or restrained due to cardiac issues. Past heart diagnosis applications are outdated and suffer from poor performance. Therefore, an intelligent heart disease diagnosis application design is required. In this research work, internet of things (IoT) sensor data with a deep learning-based heart diagnosis application is designed. The heart disease IoT sensor data is collected from the University of California Irvine machine learning repository free open-source dataset which is useful for training the deep graph convolutional network (DG_ConvoNet) deep learning network. The testing data has been collected from the Cleveland Clinic Foundation; it is a collection of 350 real-time clinical instances from heart patients through IoT sensors. The K-means technique is employed to remove noise in sensor data and clustered the unstructured data. The features are extracted to employ Linear Quadratic Discriminant Analysis. DG_ConvoNet is a deep learning process to classify and predict heart diseases. The diagnostic application achieves an accuracy of 96%, sensitivity of 80%, specificity of 73%, precision of 90%, F-Score of 79%, and area under the ROC curve of 75% implementing the proposed model.
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Affiliation(s)
- K. Saikumar
- Department of ECE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, India
| | - V. Rajesh
- Department of ECE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, India
| | - Gautam Srivastava
- Department of Mathematics and Computer Science, Brandon University, Brandon, MB, Canada
- Research Centre for Interneural Computing, China Medical University, Taichung, Taiwan
- Department of Mathematics and Computer Science, Lebanese American University, Beirut, Lebanon
| | - Jerry Chun-Wei Lin
- Western Norway University of Applied Science, Bergen, Norway
- *Correspondence: Jerry Chun-Wei Lin,
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Woo JH, Kim EC, Kim SM. The Current Status of Breakthrough Devices Designation in the United States and Innovative Medical Devices Designation in Korea for Digital Health Software. Expert Rev Med Devices 2022; 19:213-228. [PMID: 35255755 DOI: 10.1080/17434440.2022.2051479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Artificial Intelligence (AI) is becoming increasingly utilized in the medical device industry as it can address unmet demands in clinical sites and provide more patient treatment options. This study aims to analyze the FDA's Breakthrough Device Program and MFDS' Innovative Medical Device Program, which support regulatory science for innovative medical devices today. Through this study, it is intended to enable prediction of current development trends of Software as a Medical Device (SaMD) and Digital Therapeutics (DTx), which combine AI and technologies to be used in the clinical field soon. AREAS COVERED A systematic search was conducted on the broad topics of "FDA and MFDS Program's SaMD, DTx". A parallel review and update of PubMed, and the official websites were conducted to investigate the regulator's databases, review official press releases of regulatory agencies, and provide detailed descriptions of researchers. EXPERT OPINION The efforts of related stakeholders are needed to expand AI technology to diagnosis, prevention, and treatment technologies for diseases that are difficult to diagnose early or are classified as clinical challenges. It is important to prepare regulatory policies suitable for the rapid pace of technological development and to create an environment where regulatory science can be realized by developers.
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Affiliation(s)
- Jae Hyun Woo
- Research Institute for Commercialization of Biomedical Convergence Technology, Seoul, Republic of Korea.,Medical Device Industry Program in Graduate School, Dongguk University, Seoul, Republic of Korea.,National Institute of Medical Device Safety Information, Seoul, Republic of Korea.,Department of Medical Biotechnology, Dongguk University-Seoul, Seoul, Korea
| | - Eun Cheol Kim
- Research Institute for Commercialization of Biomedical Convergence Technology, Seoul, Republic of Korea.,Medical Device Industry Program in Graduate School, Dongguk University, Seoul, Republic of Korea.,National Institute of Medical Device Safety Information, Seoul, Republic of Korea.,Department of Medical Biotechnology, Dongguk University-Seoul, Seoul, Korea
| | - Sung Min Kim
- Research Institute for Commercialization of Biomedical Convergence Technology, Seoul, Republic of Korea.,Medical Device Industry Program in Graduate School, Dongguk University, Seoul, Republic of Korea.,National Institute of Medical Device Safety Information, Seoul, Republic of Korea.,Department of Medical Biotechnology, Dongguk University-Seoul, Seoul, Korea
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Cloud Computing Image Recognition System Assists the Construction of the Internet of Things Model of Administrative Management Event Parameters. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:8630256. [PMID: 34956357 PMCID: PMC8702353 DOI: 10.1155/2021/8630256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/18/2021] [Accepted: 11/26/2021] [Indexed: 11/18/2022]
Abstract
In order to successfully apply the Internet of Things and cloud computing to the administrative management of spatial structures and realize the systematization, digitization, and intelligence of administrative management, this article draws on research experience in related fields and considers the data characteristics and computing tasks of administrative management. The whole cycle of transmission, storage, postprocessing, and visualization is the main line of research, and a cloud computing-based spatial structure administrative management IoT system is constructed. First, by summarizing the application status of the Internet of Things, the general Internet of Things system is summarized into three levels, and combined with the specific work in the spatial structure administrative management, the overall framework of the spatial structure administrative management of the Internet of Things system is proposed, and the functional sublayers are carried out. Secondly, in response to the above problems, through the traditional image recognition system research and practical application investigation, in order to meet the user's requirements for the computing efficiency and recognition accuracy of the image recognition system, an image recognition system in the cloud computing environment is proposed. It proposes a fuzzy evaluation algorithm of health grade hierarchy analysis optimized for the index system and scoring system and a calculation method that uses time series to identify regular outliers. The optical image pixel-level fusion method and the infrared and visible image fusion method based on complementary information are proposed, and the image fusion software is developed. Finally, in order to enable the application layer to use cluster resources to efficiently and intelligently process massive monitoring data containing redundancy, heterogeneity, anomalies, and many other defects, according to the calculation process of each specific task of data preprocessing and postprocessing in the application layer, demonstrations are made one by one. After analysis, it is concluded that vertical storage of data blocks according to different sensor channels is the optimal strategy.
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Zhu S, Saravanan V, Muthu B. Achieving data security and privacy across healthcare applications using cyber security mechanisms. ELECTRONIC LIBRARY 2020. [DOI: 10.1108/el-07-2020-0219] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
Currently, in the health-care sector, information security and privacy are increasingly important issues. The improvement in information security is highlighted in adopting digital patient records based on regulation, providers’ consolidation, and the growing need to exchange information among patients, providers, and payers.
Design/methodology/approach
Big data on health care are likely to improve patient outcomes, predict epidemic outbreaks, gain valuable insights, prevent diseases, reduce health-care costs and improve analysis of the quality of life.
Findings
In this paper, the big data analytics-based cybersecurity framework has been proposed for security and privacy across health-care applications. It is vital to identify the limitations of existing solutions for future research to ensure a trustworthy big data environment. Furthermore, electronic health records (EHR) could potentially be shared by various users to increase the quality of health-care services. This leads to significant issues of privacy that need to be addressed to implement the EHR.
Originality/value
This framework combines several technical mechanisms and environmental controls and is shown to be enough to adequately pay attention to common threats to network security.
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Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:130820-130839. [PMID: 34812339 PMCID: PMC8545324 DOI: 10.1109/access.2020.3009328] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 07/11/2020] [Indexed: 05/18/2023]
Abstract
The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.
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Affiliation(s)
- Quoc-Viet Pham
- Research Institute of Computer, Information and CommunicationPusan National UniversityBusan46241South Korea
| | - Dinh C. Nguyen
- School of EngineeringDeakin UniversityWaurn PondsVIC3216Australia
| | - Thien Huynh-The
- ICT Convergence Research CenterKumoh National Institute of TechnologyGumi39177South Korea
| | - Won-Joo Hwang
- Department of Biomedical Convergence EngineeringPusan National UniversityBusan46241South Korea
- Department of Information Convergence Engineering (Artificial Intelligence)Pusan National UniversityBusan46241South Korea
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Ma Y, Ping K, Wu C, Chen L, Shi H, Chong D. Artificial Intelligence powered Internet of Things and smart public service. LIBRARY HI TECH 2019. [DOI: 10.1108/lht-12-2017-0274] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The Internet of Things (IoT) has attracted a lot of attention in both industrial and academic fields for recent years. Artificial intelligence (AI) has developed rapidly in recent years as well. AI naturally combines with the Internet of Things in various ways, enabling big data applications, machine learning algorithms, deep learning, knowledge discovery, neural networks and other technologies. The purpose of this paper is to provide state of the art in AI powered IoT and study smart public services in China.
Design/methodology/approach
This paper reviewed the articles published on AI powered IoT from 2009 to 2018. Case study as a research method has been chosen.
Findings
The AI powered IoT has been found in the areas of smart cities, healthcare, intelligent manufacturing and so on. First, this study summarizes recent research on AI powered IoT systematically; and second, this study identifies key research topics related to the field and real-world applications.
Originality/value
This research is of importance and significance to both industrial and academic fields researchers who need to understand the current and future development of intelligence in IoT. To the best of authors’ knowledge, this is the first study to review the literature on AI powered IoT from 2009 to 2018. This is also the first literature review on AI powered IoT with a case study of smart public service in China.
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Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals. J Med Syst 2018; 42:228. [DOI: 10.1007/s10916-018-1093-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 10/02/2018] [Indexed: 10/28/2022]
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Parthasarathy P, Vivekanandan S. A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm. ACTA ACUST UNITED AC 2018. [DOI: 10.1080/1206212x.2018.1457471] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- P. Parthasarathy
- School of Electrical Engineering, VIT University, Vellore, India
| | - S. Vivekanandan
- School of Electrical Engineering, VIT University, Vellore, India
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Manogaran G, Thota C, Lopez D. Human-Computer Interaction With Big Data Analytics. ADVANCES IN HUMAN AND SOCIAL ASPECTS OF TECHNOLOGY 2018. [DOI: 10.4018/978-1-5225-2863-0.ch001] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics.
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Abstract
Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.
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Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. MULTIMEDIA TOOLS AND APPLICATIONS 2017. [DOI: 10.1007/s11042-017-4768-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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