ABSTRACT
The COVID-19 pandemic coincided with the growth and ripeness of several digital methods, such as Artificial Intelligence (AI) (including Machine Learning (ML) and Deep Learning (DL)), internet of things (IoT), big-data analytics, Software Defined Network (SDN), robotic technology, and blockchain, etc. resulting in an experience chance for telemedicine advancement. In several nations, a telemedicine platform based on digital technology has been built and integrated into the clinical workflow in a variety of modes, including many-To-one, one-To-many, consultation mode, and practical-operation modes. These platforms are practical, efficient, and successful for exchanging epidemiological data, facilitating face-To-face interactions between patients or healthcare professionals over long distances, lowering the risk of disease transmission, and enhancing patient outcomes. This article provides a Systematic Literature Review (SLR) to call attention to the most recent advancements in evaluating COVID-19 data utilizing various methodologies such as ML, DL, SDN, and IoT. The number of studies on ML and DL provided and reviewed in this article has proven a considerable effect on the prediction and spreading of COVID-19. The main goal of this study is to show how ML, DL, IoT, and SDN may be used by researchers to provide significant solutions for authorities and healthcare statements to lessen the influence of pestilence. This report also includes many novel strategies for raising the prevalent telemedicine use. © 2022 IEEE.
ABSTRACT
In these recent years, the world has witnessed a kind of social exclusion and the inability to communicate directly due to the Corona Virus Covid 19 (COVID-19) pandemic, and the consequent difficulty of communicating with patients with hospitals led to the need to use modern technology to solve and facilitate the problem of people communicating with each other. healthcare has made many remarkable developments through the Internet of things (IoT) and cloud computing to monitor real-time patients' data, which has enabled many patients' lives to be saved. This paper presents the design and implementation of a Private Backend Server Software based on an IoT health monitoring system concerned with emergency medical services utilizing biosensors to detect multi-vital signs of an individual with an ESP32 microcontroller board and IoT cloud. The device displays the vital data, which is then uploaded to a cloud server for storage and analysis over an IoT network. Vital data is received from the cloud server and shown on the IoT medical client dashboard for remote monitoring. The proposed system allows users to ameliorate healthcare jeopardy and minify its costs by re-cording, gathering, sharing, and analyzing vast biodata streams such as Intensive Care Units (ICU) (i.e., temperature, heartbeat rate (HR), Oxygen level (SPO2), etc.), efficiently in real-time. In this proposal, the data is sent from sensors fixed in the patient body to the Web and Mobile App continually in real time for collection and analysis. The system showed impressive performance with an average disparity of less than 1%. body temperature, SPO2, and HR readings were remarkably accurate compared to the CE approval patient monitoring system. In Addition, The system was highly dependable with a success rate for IoT data broadcasts. © 2023,International journal of online and biomedical engineering. All Rights Reserved.
ABSTRACT
Background: The COVID-19 pandemic is caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) virus, which causes life-threatening illness and mortality. One of the most critical risk factors for severe COVID-19 and COVID-19 mortality is cardiovascular disease (CVD) that develops during infection. Objective: To determine the most common CVD that occurred during infection with COVID-19 patients and the link between CVD and Fate. Material and methods: A descriptive cross-sectional study was conducted between July 22 and April 10 2022 to describe the frequency of CVD among 100 COVID-19 patients, as well as to detect the serological cardiovascular markers with mortality rates of those patients who attended Al-Karama Teaching Hospital in Baghdad governorate, Al-Shafaa Hospital, and Al-Ramadi Teaching Hospital in Al-Anbar governorate. Blood samples from all of the patients were taken for cardiovascular serological markers, as per the manufacturer's instructions. Results: The mean age of the COVID-19 patients with CVD was 65 23.83.83 (83.0%). Thrombosis, heart failure, myocarditis, myocardial infarction or acute coronary syndrome, hypertension, myocardial injury, angina, and pulmonary embolism were found in 83 (83.0%) of the 100 confirmed COVID-19 cases using IgM antibodies against SARS-CoV-2 by ELIZA in the following frequencies: 21, 9, 9, 7, 10, 11, 8, 5, and 3 respectively. ELIZA discovered COVID-19 patients with CVD utilizing IgG antibodies against SARS-CoV-2 in the remaining 17 cases (17.0%). Troponin-T, Ferritin, D-dimer, Leukocyte, B Urea, CRP LDH were 58.60 ±40.70, 368.36±265.75, 2523.05±1727.60, 15.00±7.67, 103.49±60.74, 33.13±35.74, 525.40±459.86, 4.11±2.13, respectively, among COVID-19 heart failure patients. Troponin-T, Ferritin, D-dimer, Leukocyte, B Urea, CRP LDH were 33.61±34.70, 481.20±181.89, 3361.15±14.26, 20.08±10.54, 76.71±28.02, 22.76±19.73, 536.35±798.14, 3.61±2.11, respectively among COVID-19 Myocarditis patients. Conclusion: There was no significant variation in mortality across the various CVD of COVID-19 cases, There were no significant differences in cardiovascular serological markers in different age groups of among CVD of COVID-19 cases. © 2022, ResearchTrentz Academy Publishing Education Services. All rights reserved.
ABSTRACT
SARS-CoV-2, like any other virus, continues to mutate as it spreads, according to an evolutionary process. Unlike any other virus, the number of currently available sequences of SARS-CoV-2 in public databases such as GISAID is already several million. This amount of data has the potential to uncover the evolutionary dynamics of a virus like never before. However, a million is already several orders of magnitude beyond what can be processed by the traditional methods designed to reconstruct a virus's evolutionary history, such as those that build a phylogenetic tree. Hence, new and scalable methods will need to be devised in order to make use of the ever increasing number of viral sequences being collected. Since identifying variants is an important part of understanding the evolution of a virus, in this paper, we propose an approach based on clustering sequences to identify the current major SARS-CoV-2 variants. Using a k-mer based feature vector generation and efficient feature selection methods, our approach is effective in identifying variants, as well as being efficient and scalable to millions of sequences. Such a clustering method allows us to show the relative proportion of each variant over time, giving the rate of spread of each variant in different locations - something which is important for vaccine development and distribution. We also compute the importance of each amino acid position of the spike protein in identifying a given variant in terms of information gain. Positions of high variant-specific importance tend to agree with those reported by the USA's Centers for Disease Control and Prevention (CDC), further demonstrating our approach. © 2021 ACM.