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Li J, Lu X, Wang X, Singh VP, Yang C, Chen L, Zhang Y. Assessing the long-term impact of cascade hydropower development on the inundation patterns of floodplain wetlands. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 346:118948. [PMID: 37717396 DOI: 10.1016/j.jenvman.2023.118948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/28/2023] [Accepted: 09/06/2023] [Indexed: 09/19/2023]
Abstract
The inundation process of floodplain wetlands plays a crucial role in maintaining the balance of river ecosystems, which are highly sensitive to hydrological alteration. Nevertheless, the specific mechanisms through which these hydrological changes affect the inundation patterns of floodplain wetlands are still unclear. This study aimed to investigate the impact mechanism of cascade hydropower development on the inundation process of floodplain wetlands. Multitemporal remote sensing datasets and long-term hydrometeorological data series were utilized in this study. By employing the water appearance frequency (WAF) index, wetland hydrological stability assessment, and wavelet analysis, the inundation changes in floodplain wetlands and the underlying hydrological driving mechanisms were examined. The results revealed significant alterations in the inundation frequency of floodplain wetlands due to the construction of upstream dams. Specifically, the construction of the Danjiangkou and Wangfuzhou dams led to an increase in the total inundated area of Part A (16.09 km2) and Part B (76.93 km2), respectively. Conversely, the moderate frequency inundation zone in Part C decreased (26.7 km2) after the construction of the Cuijiaying Dam. The typical floodplain wetland 7 shifted from high to low (8.94 km2) stability after the construction of the Cuijiaying Dam. Furthermore, the cascade hydropower dam construction resulted in increased fluctuations in downstream water discharge. This study provides an effective approach to understanding the impact of cascade hydropower dams on the inundation process of floodplain wetlands.
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Affiliation(s)
- Jing Li
- Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430077, China; University of Chinese Academy of Sciences, Beijing, China
| | - Xiaorong Lu
- Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430077, China.
| | - Xuelei Wang
- Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430077, China.
| | - Vijay P Singh
- Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A&M University, 2117, College Station, TX, 77843, USA
| | - Chao Yang
- Ecological Environment Monitoring and Scientific Research Center, Yangtze River Basin Ecological Environment Supervision and Administration Bureau, Ministry of Ecology and Environment, Wuhan, 430010, China
| | - Liang Chen
- School of Tourism, Xinyang Normal University, Xinyang, 464000, China
| | - Yihang Zhang
- Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430077, China
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2
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Karlafti E, Anagnostis A, Simou T, Kollatou AS, Paramythiotis D, Kaiafa G, Didaggelos T, Savvopoulos C, Fyntanidou V. Support Systems of Clinical Decisions in the Triage of the Emergency Department Using Artificial Intelligence: The Efficiency to Support Triage. Acta Med Litu 2023; 30:19-25. [PMID: 37575380 PMCID: PMC10417017 DOI: 10.15388/amed.2023.30.1.2] [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: 09/21/2022] [Revised: 11/17/2022] [Accepted: 12/13/2022] [Indexed: 01/26/2023] Open
Abstract
Purpose In the Emergency Departments (ED) the current triage systems that are been implemented are based completely on medical education and the perception of each health professional who is in charge. On the other hand, cutting-edge technology, Artificial Intelligence (AI) can be incorporated into healthcare systems, supporting the healthcare professionals' decisions, and augmenting the performance of triage systems. The aim of the study is to investigate the efficiency of AI to support triage in ED. Patients–Methods The study included 332 patients from whom 23 different variables related to their condition were collected. From the processing of patient data for input variables, it emerged that the average age was 56.4 ± 21.1 years and 50.6% were male. The waiting time had an average of 59.7 ± 56.3 minutes while 3.9% ± 0.1% entered the Intensive Care Unit (ICU). In addition, qualitative variables related to the patient's history and admission clinics were used. As target variables were taken the days of stay in the hospital, which were on average 1.8 ± 5.9, and the Emergency Severity Index (ESI) for which the following distribution applies: ESI: 1, patients: 2; ESI: 2, patients: 18; ESI: 3, patients: 197; ESI: 4, patients: 73; ESI: 5, patients: 42. Results To create an automatic patient screening classifier, a neural network was developed, which was trained based on the data, so that it could predict each patient's ESI based on input variables.The classifier achieved an overall accuracy (F1 score) of 72.2% even though there was an imbalance in the classes. Conclusions The creation and implementation of an AI model for the automatic prediction of ESI, highlighted the possibility of systems capable of supporting healthcare professionals in the decision-making process. The accuracy of the classifier has not reached satisfactory levels of certainty, however, the performance of similar models can increase sharply with the collection of more data.
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Affiliation(s)
- Eleni Karlafti
- Emergency Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Athanasios Anagnostis
- Advanced Insights, Artificial Intelligence Solutions, Ipsilantou 10, Panorama, 55236 Thessaloniki, Greece
| | - Theodora Simou
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Angeliki Sevasti Kollatou
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Daniel Paramythiotis
- First Propaedeutic Surgery Department, AHEPA University General Hospital of Thessaloniki, 55636 Thessaloniki, Greece
| | - Georgia Kaiafa
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Triantafyllos Didaggelos
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Christos Savvopoulos
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Varvara Fyntanidou
- Emergency Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
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Lazebnik T, Itai U. Bounding pandemic spread by heat spread. JOURNAL OF ENGINEERING MATHEMATICS 2023; 138:6. [PMID: 36628323 PMCID: PMC9817466 DOI: 10.1007/s10665-022-10253-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
The beginning of a pandemic is a crucial stage for policymakers. Proper management at this stage can reduce overall health and economical damage. However, knowledge about the pandemic is insufficient. Thus, the use of complex and sophisticated models is challenging. In this study, we propose analytical and stochastic heat spread-based boundaries for the pandemic spread as indicated by the Susceptible-Infected-Recovered (SIR) model. We study the spread of a pandemic on an interaction (social) graph as a diffusion and compared it with the stochastic SIR model. The proposed boundaries are not requiring accurate biological knowledge such as the SIR model does.
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Affiliation(s)
- Teddy Lazebnik
- Department of Cancer Biology, Cancer Institute, University College London, London, UK
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Rohmetra H, Raghunath N, Narang P, Chamola V, Guizani M, Lakkaniga NR. AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges. COMPUTING 2023; 105. [PMCID: PMC8006120 DOI: 10.1007/s00607-021-00937-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients’ symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques.
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Affiliation(s)
- Honnesh Rohmetra
- Department of CSIS, Birla Institute of Technology and Science, Pilani, Pilani, Rajasthan India
| | - Navaneeth Raghunath
- Department of CSIS, Birla Institute of Technology and Science, Pilani, Pilani, Rajasthan India
| | - Pratik Narang
- Department of CSIS, Birla Institute of Technology and Science, Pilani, Pilani, Rajasthan India
| | - Vinay Chamola
- Department of EEE & APPCAIR, Birla Institute of Technology and Science, Pilani, Pilani, Rajasthan India
| | | | - Naga Rajiv Lakkaniga
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, USA
- SmartBio Labs, Chennai, India
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5
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Lv Z, Chen D, Feng H, Zhu H, Lv H. Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS : A PUBLICATION OF THE IEEE INTELLIGENT TRANSPORTATION SYSTEMS COUNCIL 2022; 23:25106-25114. [PMID: 36789134 PMCID: PMC9906644 DOI: 10.1109/tits.2021.3113787] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/18/2021] [Accepted: 09/15/2021] [Indexed: 05/25/2023]
Abstract
The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.
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Affiliation(s)
- Zhihan Lv
- Department of Game DesignFaculty of ArtsUppsala University752 36UppsalaSweden
| | - Dongliang Chen
- College of Computer Science and TechnologyQingdao UniversityQingdao266071China
| | - Hailin Feng
- School of Information EngineeringZhejiang A & F UniversityHangzhou311300China
| | - Hu Zhu
- College of Telecommunications and Information EngineeringNanjing University of Posts and TelecommunicationsNanjing210049China
| | - Haibin Lv
- North China Sea Offshore Engineering Survey Institute, Ministry of Natural Resources North Sea BureauQingdao266061China
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6
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Meskher H, Belhaouari SB, Thakur AK, Sathyamurthy R, Singh P, Khelfaoui I, Saidur R. A review about COVID-19 in the MENA region: environmental concerns and machine learning applications. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:82709-82728. [PMID: 36223015 PMCID: PMC9554385 DOI: 10.1007/s11356-022-23392-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus's transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination.
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Affiliation(s)
- Hicham Meskher
- Division of Process Engineering, College of Applied Science, Kasdi-Merbah University, 30000, Ouargla, Algeria
| | - Samir Brahim Belhaouari
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Education City, Qatar Foundation, P.O. Box 34110, Doha, Qatar
| | - Amrit Kumar Thakur
- Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, Tamil Nadu, 641407, India
| | - Ravishankar Sathyamurthy
- Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dammam, Saudi Arabia.
| | - Punit Singh
- Institute of Engineering and Technology, Department of Mechanical Engineering, GLA University Mathura, Mathura, Uttar Pradesh, 281406, India
| | - Issam Khelfaoui
- School of Insurance and Economics, University of International Business and Economics, Beijing, China
| | - Rahman Saidur
- Research Centre for Nano-Materials and Energy Technology (RCNMET), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500, Petaling Jaya, Malaysia
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Management of Landslides in a Rural–Urban Transition Zone Using Machine Learning Algorithms—A Case Study of a National Highway (NH-44), India, in the Rugged Himalayan Terrains. LAND 2022. [DOI: 10.3390/land11060884] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Landslides are critical natural disasters characterized by a downward movement of land masses. As one of the deadliest types of disasters worldwide, they have a high death toll every year and cause a large amount of economic damage. The transition between urban and rural areas is characterized by highways, which, in rugged Himalayan terrain, have to be constructed by cutting into the mountains, thereby destabilizing them and making them prone to landslides. This study was conducted landslide-prone regions of the entire Himalayan belt, i.e., National Highway NH-44 (the Jammu–Srinagar stretch). The main objectives of this study are to understand the causes behind the regular recurrence of the landslides in this region and propose a landslide early warning system (LEWS) based on the most suitable machine learning algorithms among the four selected, i.e., multiple linear regression, adaptive neuro-fuzzy inference system (ANFIS), random forest, and decision tree. It was found that ANFIS and random forest outperformed the other proposed methods with a substantial increase in overall accuracy. The LEWS model was developed using the land system parameters that govern landslide occurrence, such as rainfall, soil moisture, distance to the road and river, slope, land surface temperature (LST), and the built-up area (BUA) near the landslide site. The developed LEWS was validated using various statistical error assessment tools such as the root mean square error (RMSE), mean square error (MSE), confusion matrix, out-of-bag (OOB) error estimation, and area under the receiver operating characteristic (ROC) curve (AUC). The outcomes of this study can help to manage landslide hazards in the Himalayan urban–rural transition zones and serve as a sample study for similar mountainous regions of the world.
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Ahmed I, Jeon G. Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses. Interdiscip Sci 2022; 14:504-519. [PMID: 34357528 PMCID: PMC8342660 DOI: 10.1007/s12539-021-00465-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 07/18/2021] [Accepted: 07/23/2021] [Indexed: 12/01/2022]
Abstract
Recent pandemic of COVID-19 (Coronavirus) caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has been growing lethally with unusual speed. It has infected millions of people and continues a mortifying influence on the global population's health and well-being. In this situation, genome sequence analysis and advanced artificial intelligence techniques may help researchers and medical experts to understand the genetic variants of COVID-19 or SARS-CoV-2. Genome sequence analysis of COVID-19 is crucial to understand the virus's origin, behavior, and structure, which might help produce/develop vaccines, antiviral drugs, and efficient preventive strategies. This paper introduces an artificial intelligence based system to perform genome sequence analysis of COVID-19 and alike viruses, e.g., SARS, middle east respiratory syndrome, and Ebola. The system helps to get important information from the genome sequences of different viruses. We perform comparative data analysis by extracting basic information of COVID-19 and other genome sequences, including information of nucleotides composition and their frequency, tri-nucleotide compositions, count of amino acids, alignment between genome sequences, and their DNA similarity information. We use different visualization methods to analyze these viruses' genome sequences and, finally, apply machine learning based classifier support vector machine to classify different genome sequences. The data set of different virus genome sequences are obtained from an online publicly accessible data center repository. The system achieves good classification results with an accuracy of 97% for COVID-19, 96%, SARS, and 95% for MERS and Ebola genome sequences, respectively.
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Affiliation(s)
- Imran Ahmed
- Center of Excellence in IT, Institute of Management Sciences, Hayatabad, Peshawar, 25000 Khyber Pakhtunkhwa Pakistan
| | - Gwanggil Jeon
- Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea
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9
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Chamola V, Goyal A, Sharma P, Hassija V, Binh HTT, Saxena V. Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management. Neural Comput Appl 2022; 35:1-11. [PMID: 35310553 PMCID: PMC8918902 DOI: 10.1007/s00521-022-07087-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 02/14/2022] [Indexed: 11/24/2022]
Abstract
Healthcare professionals, patients, and other stakeholders have been storing medical prescriptions and other relevant reports electronically. These reports contain the personal information of the patients, which is sensitive data. Therefore, there exists a need to store these records in a decentralized model (using IPFS and Ethereum decentralized application) to provide data and identity protection. Many patients recurrently visit doctors and undergo treatments while receiving different prescriptions and reports. In case of an emergency, the doctors and attendants may need and benefit from the patients' medical history. However, they are unable to go through medical history and a wide range of previous reports and prescriptions due to time constraints. In this paper, we propose an AI-assisted blockchain-based framework in which the stored medical records (handwritten prescriptions, printed prescriptions, and printed reports) are stored and processed using various AI techniques like optical character recognition (OCR) to form a single patient medical history report. The report concisely presents only the crucial information for convenience and perusal and is stored securely over a decentralized blockchain network for later use.
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Affiliation(s)
| | - Adit Goyal
- Department of CSE and IT, JIIT, Noida, India
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10
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Karlafti E, Anagnostis A, Kotzakioulafi E, Vittoraki MC, Eufraimidou A, Kasarjyan K, Eufraimidou K, Dimitriadou G, Kakanis C, Anthopoulos M, Kaiafa G, Savopoulos C, Didangelos T. Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction. J Pers Med 2021; 11:1380. [PMID: 34945852 PMCID: PMC8705973 DOI: 10.3390/jpm11121380] [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: 11/19/2021] [Revised: 12/07/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022] Open
Abstract
Since the beginning of the COVID-19 pandemic, 195 million people have been infected and 4.2 million have died from the disease or its side effects. Physicians, healthcare scientists and medical staff continuously try to deal with overloaded hospital admissions, while in parallel, they try to identify meaningful correlations between the severity of infected patients with their symptoms, comorbidities and biomarkers. Artificial intelligence (AI) and machine learning (ML) have been used recently in many areas related to COVID-19 healthcare. The main goal is to manage effectively the wide variety of issues related to COVID-19 and its consequences. The existing applications of ML to COVID-19 healthcare are based on supervised classifications which require a labeled training dataset, serving as reference point for learning, as well as predefined classes. However, the existing knowledge about COVID-19 and its consequences is still not solid and the points of common agreement among different scientific communities are still unclear. Therefore, this study aimed to follow an unsupervised clustering approach, where prior knowledge is not required (tabula rasa). More specifically, 268 hospitalized patients at the First Propaedeutic Department of Internal Medicine of AHEPA University Hospital of Thessaloniki were assessed in terms of 40 clinical variables (numerical and categorical), leading to a high-dimensionality dataset. Dimensionality reduction was performed by applying a principal component analysis (PCA) on the numerical part of the dataset and a multiple correspondence analysis (MCA) on the categorical part of the dataset. Then, the Bayesian information criterion (BIC) was applied to Gaussian mixture models (GMM) in order to identify the optimal number of clusters under which the best grouping of patients occurs. The proposed methodology identified four clusters of patients with similar clinical characteristics. The analysis revealed a cluster of asymptomatic patients that resulted in death at a rate of 23.8%. This striking result forces us to reconsider the relationship between the severity of COVID-19 clinical symptoms and the patient's mortality.
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Affiliation(s)
- Eleni Karlafti
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
- Emergency Department, AHEPA University Hospital, Aristotle University of Thessaloniki, 54621 Thessaloniki, Greece
| | - Athanasios Anagnostis
- Advanced Insights, Artificial Intelligence Solutions, Ipsilantou 10, Panorama, 55236 Thessaloniki, Greece;
| | - Evangelia Kotzakioulafi
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Michaela Chrysanthi Vittoraki
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Ariadni Eufraimidou
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Kristine Kasarjyan
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Katerina Eufraimidou
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Georgia Dimitriadou
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Chrisovalantis Kakanis
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Michail Anthopoulos
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Georgia Kaiafa
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Christos Savopoulos
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
| | - Triantafyllos Didangelos
- First Propaedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital of Thessaloniki, 54621 Thessaloniki, Greece; (E.K.); (M.C.V.); (A.E.); (K.K.); (K.E.); (G.D.); (C.K.); (M.A.); (G.K.); (C.S.); (T.D.)
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Quy VK, Hau NV, Anh DV, Ngoc LA. Smart healthcare IoT applications based on fog computing: architecture, applications and challenges. COMPLEX INTELL SYST 2021; 8:3805-3815. [PMID: 34804767 PMCID: PMC8595960 DOI: 10.1007/s40747-021-00582-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 10/19/2021] [Indexed: 11/26/2022]
Abstract
The history of human development has proven that medical and healthcare applications for humanity always are the main driving force behind the development of science and technology. The advent of Cloud technology for the first time allows providing systems infrastructure as a service, platform as a service and software as a service. Cloud technology has dominated healthcare information systems for decades now. However, one limitation of cloud-based applications is the high service response time. In some emergency scenarios, the control and monitoring of patient status, decision-making with related resources are limited such as hospital, ambulance, doctor, medical conditions in seconds and has a direct impact on the life of patients. To solve these challenges, optimal computing technologies have been proposed such as cloud computing, edge computing, and fog computing technologies. In this article, we make a comparison between computing technologies. Then, we present a common architectural framework based on fog computing for Internet of Health Things (Fog-IoHT) applications. Besides, we also indicate possible applications and challenges in integrating fog computing into IoT Healthcare applications. The analysis results indicated that there is huge potential for IoHT applications based on fog computing. We hope, this study will be an important guide for the future development of fog-based Healthcare IoT applications.
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Affiliation(s)
- Vu Khanh Quy
- Hung Yen University of Technology and Education, Khoai Chau, Hungyen Vietnam
| | - Nguyen Van Hau
- Hung Yen University of Technology and Education, Khoai Chau, Hungyen Vietnam
| | - Dang Van Anh
- Hung Yen University of Technology and Education, Khoai Chau, Hungyen Vietnam
| | - Le Anh Ngoc
- Swinburne Vietnam, FPT University, Hanoi, Vietnam
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Leite GS, Albuquerque AB, Pinheiro PR. Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases-A Systematic Literature Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:10765. [PMID: 34682511 PMCID: PMC8535524 DOI: 10.3390/ijerph182010765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/09/2021] [Accepted: 10/11/2021] [Indexed: 12/23/2022]
Abstract
With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.
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Affiliation(s)
- Gleidson Sobreira Leite
- UNIFOR, Department of Computer Science, University of Fortaleza, Fortaleza 60811-905, Ceará, Brazil; (A.B.A.); (P.R.P.)
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