ABSTRACT
The death t toll of the coronavirus disease 2019 (COVID-19) has been considerable. Several risk factors have been linked to mortality due to COVID-19 in hospitals. This study aimed to describe the clinical characteristics of patients who either died from COVID-19 at Dhaka Medical College Hospital in Bangladesh. In this retrospective study, we reviewed the hospital records of patients who died or recovered and tested positive for COVID-19 from May 3 to August 31, 2020. All patients who died during the study period were included in the analysis. A comparison group of patients who survived COVID-19 at the same hospital during the same period was systematically sampled. All available information was retrieved from the records, including demographic, clinical, and laboratory variables. Of the 3115 patients with confirmed COVID-19 during the study period, 282 died.The mean age of patients who died was higher than that of those who survived (56.7 vs 52.6 years). Approximately three-fourths of deceased patients were male. History of smoking (risk ratio 2.3;95% confidence interval: 1.6-3.4), comorbidities (risk ratio: 1.5;95% confidence interal:1.1-2.1), chronic kidney disease (risk ratio: 3.2;95% confidence interval: 1.7-6.25), and ischemic heart disease (risk ratio:1.8;95% confidence interval: 1.1-2.9) were higher among the deceased than among those who survived. Mean C-reactive protein and D-dimer levels [mean (interquartile range), 34 (21-56) vs. 24 (12-48);and D-dimer [1.43 (1-2.4) vs. 0.8 (0.44-1.55)] were higher among those who died than among those who recovered. Older age, male sex, rural residence, history of smoking, and chronic kidney disease were found to be important predictors of mortality. Early hospitalization should be considered for patients with COVID-19 who are older, male, and have chronic kidney disease. Rapid referral to tertiary care facilities is necessary for high-risk patients in rural settings.Copyright © 2023 Hoque MM.
ABSTRACT
The Covid-19 pandemic imposes a significant impact on human life. Due to the pandemic, all over the globe is reducing physical communication and increasing virtual communication (e.g., Online platforms). Most people are sharing and consuming their information through online platforms (i.e., news portals, blogs and social media). The online platforms are producing different aspects of information, including Covid-19. However, Covid-19 information mining from the English textual data is an evolving research task during the Covid-19 pandemic and post-pandemic period. In this regard, this research introduces a Covid-19 text mining system (named CovTexMiner). The CovTexMiner comprises three main modules: (i) Covid-19 corpus development, (ii) Covid-19 text to feature extraction and (iii) CNN-based Covid-19 text mining. The Covid-19 corpus development module developed two types of corpus: a domain-specific GloVe embedding corpus for English text (ECovE) and a classification corpus (ECovC). The Covid-19 text to feature extraction module extracts the more Covid-19-affiliated text features using domain-specific GloVe embedding. At the same time, the CNN-based text mining module trained a binary classifier, which intelligent mining a piece of text contains the Covid-19 information or not. The proposed CovTexMiner obtained maximum accuracy of 88.89% on the developed corpus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In this paper, we present a framework that automatically labels latent Dirichlet allocation (LDA) generated topics using sentiment and aspect terms from COVID-19 tweets to help the end-users by minimizing the cognitive overhead of identifying key topics labels. Social media platforms, especially Twitter, are considered as one of the most influential sources of information for providing public opinion related to a critical situation like the COVID-19 pandemic. LDA is a popular topic modelling algorithm that extracts hidden themes of documents without assigning a specific label. Thus, automatic labelling of LDA-generated topics from COVID-19 tweets is a great challenge instead of following the manual labelling approach to get an overview of wider public opinion. To overcome this problem, in this paper, we propose a framework named SATLabel that effectively identifies significant topic labels using top unigrams features of sentiment terms and aspect terms clusters from LDA-generated topics of COVID-19-related tweets to uncover various issues related to the COVID-19 pandemic. The experimental results show that our methodology is more effective, simpler, and traces better topic labels compare to the manual topic labelling approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Background: The health care workers’(HCWs) are working 24/7 in managing devastating pandemic Corona virus disease19(COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) as front liner which leads them to be at highest risk for contacting infection. In Bangladesh, being a lower middle-income country and densely populated, the burden is much more on HCWs. Methods: We did a cross-sectional study with an aim to identify the prevalence, risk factors, and outcomes of SARS-CoV-2 infection among the HCWs in a COVID-19 dedicated tertiary care hospital. Statistical analysis was done in SPSS version-26. Multivariate regression analysis was done to evaluate risk factors responsible for COVID-19 infection and the severity of the COVID-19 disease. We expressed odds ratio with 95% CI, and considered the p-value of <0.05 as significant in the two-tailed test. Results: A total of 864 HCWs had participated with mean age of 34.16 ± 6.77 and 426 (49.31%) males. Among them 143 (16.55%) were tested RT-PCR positive for SARS-COV-2. Bronchial asthma/COPD and Hypertension were the most common co-morbidities with 23 (16.08%) for each. About 102 (71.33%) of the RT-PCR positive HCWs became symptomatic. Fever, cough and myalgia were the most common symptoms 84(82.35%), 67(65.69%) and 52(50.98%) respectively. Multivariate regression analysis revealed hypertension, gout, and working in the COVID-19 confirmed ward had a significant odds ratio for getting infected with SARS-CoV-2 [95% CI, p-value 1.91 (1.08-3.41), 0.027;5.85 (1.33-25.74), 0.020;and 1.83 (1.10-3.03), 0.019]. Bronchial asthma/COPD and gout found to be risk factors for moderate to severe COVID-19 disease [95% CI, p-value 3.04 (1.01-9.21), 0.049 and 23.38 (3.42-159.72), 0.001]. Hospitalization rate was 12(85.7%), and 3(100%) and median hospital stays were 11 (5.5-15), and 20 (7-30) days for moderate and severe diseases respectively. Outcome was uneventful without any ICU admission and death. Conclusion: HCWs working in the COVID-19 confirmed ward are at increased risk of infection with SARS-COV-2. Some co-morbidities like hypertension and gout are important risk factors for contacting SARS-COV-2 infection. Bronchial asthma/COPD and gout favors disease severity.