ABSTRACT
To evaluate the symptoms and laboratory parameters of patients admitted to the emergency department after CoronaVac (Sinovac) vaccination. Complaints, demographic characteristics, laboratory tests, interventions, and outcomes of patients admitted to the emergency department within seven days of receiving the CoronaVac (Sinovac) were all evaluated. Pain at the injection site, swelling, redness, and pain in the injected arm were expected side effects of the vaccination. In this study, a total of six people had syncope following the vaccination, one of whom developed an allergic reaction. The most common adverse events in non-geriatrics were fatigue, headache, fever, and abdominal pain, while in geriatric patients' fatigue, headache, chest pain, and dyspnea were most common, with fever falling in the last place. Rare symptoms following vaccination included syncope and urticaria. It is necessary to be prepared for the side effects that may occur during mass vaccination against COVID-19. In this study, the body's response to the vaccine in geriatric patients was atypical in vital signs and laboratory values. © 2023 Ondokuz Mayis Universitesi. All rights reserved.
ABSTRACT
The world is currently facing a significant global health crisis that threatens millions of lives. Researchers and scholars have united to create a network and knowledge maps for the novel crisis of coronavirus (COVID-19) pandemic. During a pandemic, identifying the fields of scientific study that have attracted greater research attention with the pandemic is of paramount importance in managing the pandemic. This paper aims to identify a number of popular subjects of research related to COVID-19 through topic modeling, as well as to demonstrate the role of information systems (IS) to raise awareness of new research. In this context, a corpus of 7,395 English articles related to COVID-19 published in the Web of Science database was analyzed by using the Latent Dirichlet Allocation (LDA) and the topics were identified, which are classified into five main themes: epidemiological studies, clinical studies, global impacts of COVID-19, guidelines regarding the challenges of COVID-19, and the role of information systems to manage the COVID-19 outbreak. The findings suggest that epidemiological and clinical studies are the core research topics in this respect, in line with the findings of a number of previous studies, while global impacts of COVID-19, guidelines regarding the challenges of COVID-19, and use of information systems in COVID-19 pandemic are new trend topics of COVID-19 research. © 14th IADIS International Conf. Infor. Sys. 2021. All rights reserved.
ABSTRACT
Since December 2019 the world is infected by COVID-19 or Coronavirus disease, which spreads very quickly, out of control. The high number of precautions for laboratory access, which need to be taken to contain the virus, together with the difficulties in running the gold standard test for COVID-19, result in a practical incapability to make early diagnosis. Recent advances in deep learning algorithms allow efficient implementation of computer-aided diagnosis. This paper investigates on the performance of a very well known residual network, ResNet50, and a lightweight Atrous CNN (ACNN) network using a Weighted Cross-entropy (WCE) loss function, to alleviate imbalance on COVID datasets. As a result, ResNet50 model initialized with pre-trained weights fine-tuned by ImageNet dataset and exploiting WCE achieved the state-of-the-art performance on COVIDXRay-5K test set, with a top balanced accuracy of 99.87%. © 2020 IEEE.