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Meri A, Dauwed M, Kareem HM, Hasan MK. Technology Applications in Tracking 2019-nCoV and Defeating Future Outbreaks: Iraqi Healthcare Industry in IoT Remote. WIRELESS PERSONAL COMMUNICATIONS 2023:1-17. [PMID: 37360133 PMCID: PMC10019381 DOI: 10.1007/s11277-023-10358-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/25/2023] [Indexed: 06/28/2023]
Abstract
A serious effect on people's life, social communication, and surely on medical staff who were forced to monitor their patients' status remotely relying on the available technologies to avoid potential infections and as a result reducing the workload in hospitals. this research tried to investigate the readiness level of healthcare professionals in both public and private Iraqi hospitals to utilize IoT technology in detecting, tracking, and treating 2019-nCoV pandemic, as well as reducing the direct contact between medical staff and patients with other diseases that can be monitored remotely.A cross-sectional descriptive research via online distributed questionnaire, the sample consisted of 113 physicians and 99 pharmacists from three public and two private hospitals who randomly selected by simple random sampling. The 212 responses were deeply analyzed descriptively using frequencies, percentages, means, and standard deviation.The results confirmed that the IoT technology can facilitate patient follow-up by enabling rapid communication between medical staff and patient relatives. Additionally, remote monitoring techniques can measure and treat 2019-nCoV, reducing direct contact by decreasing the workload in healthcare industries. This paper adds to the current healthcare technology literature in Iraq and middle east region an evidence of the readiness to implement IoT technology as an essential technique. Practically, it is strongly advised that healthcare policymakers should implement IoT technology nationwide especially when it comes to safe their employees' life.Iraqi medical staff are fully ready to adopt IoT technology as they became more digital minded after the 2019-nCoV crises and surely their knowledge and technical skills will be improved spontaneously based on diffusion of innovation perspective.
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Affiliation(s)
- Ahmed Meri
- Department of Medical Instrumentation Techniques Engineering, Al-Hussain University College, Karbala, 56001 Iraq
| | - Mohammed Dauwed
- Department of Computer Science, College of Science, University of Baghdad, Baghdad, 10022 Iraq
| | | | - Mohammad Khatim Hasan
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia
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Lederman R, Ben-Assuli O, Vo TH. The role of the Internet of Things in Healthcare in supporting clinicians and patients: A narrative review. HEALTH POLICY AND TECHNOLOGY 2021. [DOI: 10.1016/j.hlpt.2021.100552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Huang F, Brouqui P, Boudjema S. How does innovative technology impact nursing in infectious diseases and infection control? A scoping review. Nurs Open 2021; 8:2369-2384. [PMID: 33765353 PMCID: PMC8363394 DOI: 10.1002/nop2.863] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 01/28/2021] [Accepted: 03/04/2021] [Indexed: 12/22/2022] Open
Abstract
Aim Considering the increasing number of emerging infectious diseases, innovative approaches are strongly in demand. Additionally, research in this field has expanded exponentially. Thus, faced with this diverse information, we aim to clarify key concepts and knowledge gaps of technology in nursing and the field of infectious diseases. Design This scoping review followed the methodology of scoping review guidance from Arksey and O’Malley. Methods Six databases were searched systematically (PubMed, Web of Science, IEEE Explore, EBSCOhost, Cochrane Library and Summon). After the removal of duplicates, 532 citations were retrieved and 77 were included in the analysis. Results We identified five major trends in technology for nursing and infectious diseases: artificial intelligence, the Internet of things, information and communications technology, simulation technology and e‐learning. Our findings indicate that the most promising trend is the IoT because of the many positive effects validated in most of the reviewed studies.
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Affiliation(s)
- Fanyu Huang
- IRD, MEPHI, IHU-Méditerranée Infection, Aix Marseille Université, Marseille, France
| | - Philippe Brouqui
- IRD, MEPHI, IHU-Méditerranée Infection, Aix Marseille Université, Marseille, France.,AP-HM, IHU-Méditerranée Infection, Marseille, France
| | - Sophia Boudjema
- IRD, MEPHI, IHU-Méditerranée Infection, Aix Marseille Université, Marseille, France
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Malik YS, Sircar S, Bhat S, Ansari MI, Pande T, Kumar P, Mathapati B, Balasubramanian G, Kaushik R, Natesan S, Ezzikouri S, El Zowalaty ME, Dhama K. How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future. Rev Med Virol 2020; 31:1-11. [PMID: 33476063 PMCID: PMC7883226 DOI: 10.1002/rmv.2205] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 11/27/2020] [Accepted: 11/29/2020] [Indexed: 12/16/2022]
Abstract
The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid‐19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI‐based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid‐19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI‐based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid‐19 cases. AI‐based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid‐19 pandemic.
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Affiliation(s)
- Yashpal Singh Malik
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India.,College of Animal Biotechnology, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab, India
| | - Shubhankar Sircar
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
| | - Sudipta Bhat
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
| | - Mohd Ikram Ansari
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
| | - Tripti Pande
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
| | - Prashant Kumar
- Amity Institute of Virology and Immunology, Amity University, Noida, Uttar Pradesh, India
| | - Basavaraj Mathapati
- Polio Virus Group, Microbial Containment Complex, I.C.M.R. National Institute of Virology, Pune, Maharashtra, India
| | - Ganesh Balasubramanian
- Laboratory Division, Indian Council of Medical Research -National Institute of Epidemiology, Ministry of Health & Family Welfare, Chennai, Tamil Nadu, India
| | - Rahul Kaushik
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa, Japan
| | | | - Sayeh Ezzikouri
- Viral Hepatitis Laboratory, Virology Unit, Institut Pasteur du Maroc, Casablanca, Morocco
| | - Mohamed E El Zowalaty
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah, UAE.,Zoonosis Science Center, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
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Jagadeeswari V, Subramaniyaswamy V, Logesh R, Vijayakumar V. A study on medical Internet of Things and Big Data in personalized healthcare system. Health Inf Sci Syst 2018; 6:14. [PMID: 30279984 PMCID: PMC6146872 DOI: 10.1007/s13755-018-0049-x] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 08/12/2018] [Indexed: 12/26/2022] Open
Abstract
Personalized healthcare systems deliver e-health services to fulfill the medical and assistive needs of the aging population. Internet of Things (IoT) is a significant advancement in the Big Data era, which supports many real-time engineering applications through enhanced services. Analytics over data streams from IoT has become a source of user data for the healthcare systems to discover new information, predict early detection, and makes decision over the critical situation for the improvement of the quality of life. In this paper, we have made a detailed study on the recent emerging technologies in the personalized healthcare systems with the focus towards cloud computing, fog computing, Big Data analytics, IoT and mobile based applications. We have analyzed the challenges in designing a better healthcare system to make early detection and diagnosis of diseases and discussed the possible solutions while providing e-health services in secure manner. This paper poses a light on the rapidly growing needs of the better healthcare systems in real-time and provides possible future work guidelines.
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Affiliation(s)
- V. Jagadeeswari
- School of Computing, SASTRA Deemed University, Thanjavur, India
| | | | - R. Logesh
- School of Computing, SASTRA Deemed University, Thanjavur, India
| | - V. Vijayakumar
- School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India
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Majumdar A, Debnath T, Sood SK, Baishnab KL. Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment. J Med Syst 2018; 42:187. [PMID: 30173290 PMCID: PMC7088392 DOI: 10.1007/s10916-018-1041-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 08/20/2018] [Indexed: 11/23/2022]
Abstract
Kyasanur Forest Disease (KFD) is a life-threatening tick-borne viral infectious disease endemic to South Asia and has been taking so many lives every year in the past decade. But recently, this disease has been witnessed in other regions to a large extent and can become an epidemic very soon. In this paper, a new fog computing based e-Healthcare framework has been proposed to monitor the KFD infected patients in an early phase of infection and control the disease outbreak. For ensuring high prediction rate, a novel Extremal Optimization tuned Neural Network (EO-NN) classification algorithm has been developed using hybridization of the extremal optimization with the feed-forward neural network. Additionally, a location based alert system has also been suggested to provide the global positioning system (GPS)-based location information of each KFD infected user and the risk-prone zones as early as possible to prevent the outbreak. Furthermore, a comparative study of proposed EO-NN with state of art classification algorithms has been carried out and it can be concluded that EO-NN outperforms others with an average accuracy of 91.56%, a sensitivity of 91.53% and a specificity of 97.13% respectively in classification and accurate identification of risk-prone areas.
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Affiliation(s)
- Abhishek Majumdar
- Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India.
| | - Tapas Debnath
- Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India
| | - Sandeep K Sood
- Guru Nanak Dev University, Regional Campus, Gurdaspur, India
| | - Krishna Lal Baishnab
- Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
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