ABSTRACT
The study aims to analyze the change in coverage of health issues awareness, printed on the front page of Indian e-papers (The Hindustan Times and The Times of India) for the pre- and peri-coronavirus period. The collected news articles are examined by performing the latent dirichlet allocation algorithm. The sentiment analysis is performed to analyze the change in the emotions aroused from news articles. The outcome regarding the pre-coronavirus period reveals that the focus of the e-papers was mostly on politics, crime, and economy whereas, in the peri-coronavirus period, the e-papers are focusing more (i.e. 40% topics) on publishing the news related to disseminating the awareness about the coronavirus disease. The priority of news topics includes the active number of cases, medical facilities, and COVID-19 testing. The outcome regarding sentiment analysis reveals that negative sentiments are prominent in the peri-coronavirus period due to fear of the outbreak of the virus.
ABSTRACT
Objective: To increase the awareness of neurological complications of arteriovenous malformation (AVM) due to obstruction of the venous drainage despite being on anticoagulants. Background: Cerebral AVMs are high-flow intracranial vascular malformation comprised of feeding arteries, a nidus of vessels with intervening brain parenchyma through which arteriovenous shunting occurs and dilated draining veins allowing significant hemodynamic gradient without an interposed resistance. Venous drainage stenosis or occlusion will increase the hemodynamic pressure gradient within the AVM compartments and potentially lead to redistribution of flow resulting in cerebral venous sinus thrombosis or hemorrhagic stroke from nidus rupture. This effect might be worsened in the presence of a generalized hypercoagulable state causing microvascular injury and thrombosis;despite adequate anticoagulation therapy. Design/Methods: N/A Results: 66-year-old obese woman with history of atrial fibrillation, coronary artery disease, diabetes, hypertension, hyperlipidemia, prior stroke, small left frontal AVM diagnosed on conventional angiogram and recent COVID-19 infection presented to our comprehensive stroke center with seizures and right hemiparesis. MRI brain showed T2/FLAIR hyperintense lesion in the left frontal/parasagittal region with an extensive vasogenic edema, heterogeneous diffusion restriction, and gyriform contrast enhancement. Conventional angiogram showed AVM without nidus opacification but with an associated mass effect correlating with parenchyma edema and early venous shunting. Patient was initially misdiagnosed as low-grade neoplasm although accurate diagnosis of left parasagittal frontal venous infarct in the setting of spontaneous venous thrombosis of left frontal AVM was made with conventional angiogram. Conclusions: Venous infarct due to CVST is a devastating complication of AVM. The hemodynamic pressure gradient within the AVM might play a larger role in contributing to hypercoagulable state within the venous system leading to cerebral venous sinus thrombosis despite patient being on therapeutic anticoagulation.
ABSTRACT
As the coronavirus (COVID-19) grows its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. This perspective paper is written to capture and analyze the various mental state health issues being perceived via emotional analysis of Twitter data during the COVID-19 virus outbreak from a single nation further spread of to the whole world. A data-driven approach with higher accuracy as here can be very useful for a proactive response from the government and citizens. In the proposed work, tweets during the COVID situation have been collected and their sentiments are explored using BERT (Bidirectional Encoder Representation from Transformer) algorithm. BERT is the algorithm that takes text as input, and the trained basis on the epochs (number of passes performed). The performance parameters are computed such as accuracy, precision, recall, and F-measure. Further, the proposed approach is compared with other existing algorithms such as Naïve Bayes (NB), support vector machine (SVM), and logistic regression (LR). The performance measures indicate that the BERT algorithm outperforms all other existing algorithms with an accuracy of 86.7% as compared to 67.3%, 63.4%, and 61.2% with Naïve Bayes, support vector machine, and logistic regression, respectively. The government and other medical health agencies can use the outcomes of this paper for implementing and taking preventative measures to maintain the good mental and physical health of medical staff. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Mental health issues are very prominent in the modern world. Almost one in four people in the world are suffering from mental illness. The world has been going through a major pandemic known as a coronavirus which was first reported in China. People are at risk of getting mentally ill due to various restrictions imposed such as lockdown and social distancing. Our study aims to analyze the mental suffering of the people toward the outbreak. The analysis is done using tweets from ten different countries. For analyzing, artificial intelligence is used under which the sentiment analysis is performed illustrating fear is the most prominent emotion prevailing in the people followed by other negative emotions like sadness, anxiety, disgust, and anger. The correlation analysis indicates that negative emotion like fear is highly correlated with other negative emotions. It pinpoints the fact that the spreading of coronavirus has widely affected the mental health of the people. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Oculoplastic surgeries encompass both emergency surgeries for traumatic conditions and infectious disorders as well as elective aesthetic procedures. The COVID-19 pandemic has brought about a drastic change in this practice. Given the highly infectious nature of the disease as well as the global scarcity of medical resources;it is only prudent to treat only emergent conditions during the pandemic as we incorporate evidence-based screening and protective measures into our practices. This manuscript is a compilation of evidence-based guidelines for surgical procedures that oculoplastic surgeons can employ during the COVID-19 pandemic. These guidelines also serve as the basic framework upon which further recommendations may be based on in the future, as elective surgeries start being performed on a regular basis.
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Purpose: The novel coronavirus has not only caused significant illness and loss of life, it has caused major disruption at local, national and global levels. While the healthcare industry is experiencing growth during the pandemic, disruption to travel has affected medical tourism. This article considers the short-term factors affecting medical tourism and how they could be mitigated by incorporating technological advances to secure long-term growth. Design/methodology/approach: The study examines data provided by the Indian government as well as from non-government sources available in the public domain to review the impact of coronavirus disease 2019 (COVID-19) on medical tourism. The authors also examine data on technological advances in the healthcare industry that could help to reduce the impact of the pandemic. Findings: This study’s findings show that while in-person services have been seriously impacted in the short term, technological adaptation of medical services to facilitate remote medical consultation has significantly increased. This has enlarged the business opportunities available to hospitals and general practitioners, and it could be leveraged to enhance medical tourism. Originality/value: The article provides an analysis of the impact of the pandemic on medical tourism and how technology could be used to overcome short-term negative impacts and support longer-term development. © 2021, Emerald Publishing Limited.
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During the spread of an infectious disease such as COVID-19, the identification of human factors that affect the spread is a really important area of research. These factors directly impact the spread of such a disease and are important in identifying the various regions that are at a higher risk than others. This allows for an optimal distribution of resources according to predicted demand. Traditional infectious modeling techniques are good at representing the spread and can incorporate multiple factors that resemble real-life scenarios. The primary issue here is the identification of relevant variables. In this study, a residual analysis is presented to downsize the dataset available and shortlist the variables classified as absolutely necessary for disease modeling. The performance of different datasets is evaluated using an Artificial Neural Network and regression analysis. The results show that the drop in performance using the reduced dataset is reasonable as it is very difficult to obtain a perfect dataset covering only necessary variables. This approach can be automated in the future as it offers a small dataset containing a few variables against a large dataset with possibly hundreds of variables. © 2020 IEEE.
ABSTRACT
Many machine learning methods are being developed to predict the spread of COVID-19. This paper focuses on the expansion of inputs that may be considered in these models. A correlation matrix is used to identify those variables with the highest correlation to COVID-19 cases. These variables are then used and compared in three methods that predict future cases: a Support Vector Machine Regression (SVR), Multidimensional Regression with Interactions, and the Stepwise Regression method. All three methods predict a rise in cases similar to the actual rise in cases, and importantly, are all able to predict to a certain degree the unexpected dip in cases on the 10th and 11th day of prediction. © 2020 IEEE.