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1.
Database (Oxford) ; 20222022 Jul 15.
Article in English | MEDLINE | ID: covidwho-1948247

ABSTRACT

In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system's performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1-score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus. Database URL: https://www.ncbi.nlm.nih.gov/research/coronavirus/.


Subject(s)
COVID-19 , Data Mining , Data Mining/methods , Humans , Machine Learning , Medical Subject Headings , PubMed
2.
J Clin Med ; 10(22)2021 Nov 15.
Article in English | MEDLINE | ID: covidwho-1524037

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has impacted emergency department (ED) practice, including the treatment of traumatic brain injury (TBI), which is commonly encountered in the ED. Our study aimed to evaluate TBI treatment efficiency in the ED during the COVID-19 pandemic. A retrospective observational study was conducted using the electronic medical records from three hospitals in metropolitan Taipei, Taiwan. The time from ED arrival to brain computed tomography (CT) and the time from ED arrival to surgical management were used as measures of treatment efficiency. TBI treatment efficiencies in the ED coinciding with a small-scale local COVID-19 outbreak in 2020 (P1) and large-scale community spread in 2021 (P2) were compared against the pre-pandemic efficiency recorded in 2019. The interval between ED arrival and brain CT was significantly shortened during P1 and P2 compared with the pre-pandemic interval, and no significant delay between ED arrival and surgical management was found, indicating increased treatment efficiency for TBI in the ED during the COVID-19 pandemic. Minimizing viral spread in the community and the hospital is vital to maintaining ED treatment efficiency and capacity. The ED should retain sufficient capacity to treat older patients with serious TBI during the COVID-19 pandemic.

3.
J Clin Med ; 10(16)2021 Aug 09.
Article in English | MEDLINE | ID: covidwho-1348656

ABSTRACT

BACKGROUND AND AIMS: The coronavirus disease 2019 (COVID-19) increases hyperinflammatory state, leading to acute lung damage, hyperglycemia, vascular endothelial damage, and a higher mortality rate. Metformin is a first-line treatment for type 2 diabetes and is known to have anti-inflammatory and immunosuppressive effects. Previous studies have shown that metformin use is associated with decreased risk of mortality among patients with COVID-19; however, the results are still inconclusive. This study investigated the association between metformin and the risk of mortality among diabetes patients with COVID-19. METHODS: Data were collected from online databases such as PubMed, EMBASE, Scopus, and Web of Science, and reference from the most relevant articles. The search and collection of relevant articles was carried out between 1 February 2020, and 20 June 2021. Two independent reviewers extracted information from selected studies. The random-effects model was used to estimate risk ratios (RRs), with a 95% confidence interval. RESULTS: A total of 16 studies met all inclusion criteria. Diabetes patients given metformin had a significantly reduced risk of mortality (RR, 0.65; 95% CI: 0.54-0.80, p < 0.001, heterogeneity I2 = 75.88, Q = 62.20, and τ2 = 0.06, p < 0.001) compared with those who were not given metformin. Subgroup analyses showed that the beneficial effect of metformin was higher in the patients from North America (RR, 0.43; 95% CI: 0.26-0.72, p = 0.001, heterogeneity I2 = 85.57, Q = 34.65, τ2 = 0.31) than in patients from Europe (RR, 0.67; 95% CI: 0.47-0.94, p = 0.02, heterogeneity I2 = 82.69, Q = 23.11, τ2 = 0.10) and Asia (RR, 0.90; 95% CI: 0.43-1.86, p = 0.78, heterogeneity I2 = 64.12, Q = 11.15, τ2 = 0.40). CONCLUSIONS: This meta-analysis shows evidence that supports the theory that the use of metformin is associated with a decreased risk of mortality among diabetes patients with COVID-19. Randomized control trials with a higher number of participants are warranted to assess the effectiveness of metformin for reducing the mortality of COVID-19 patients.

4.
J Clin Med ; 10(9)2021 May 02.
Article in English | MEDLINE | ID: covidwho-1224038

ABSTRACT

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges.

5.
JMIR Med Inform ; 9(4): e21394, 2021 Apr 29.
Article in English | MEDLINE | ID: covidwho-1150636

ABSTRACT

BACKGROUND: The COVID-19 outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While analysis of nasal and throat swabs from patients is the main way to detect COVID-19, analyzing chest images could offer an alternative method to hospitals, where health care personnel and testing kits are scarce. Deep learning (DL), in particular, has shown impressive levels of performance when analyzing medical images, including those related to COVID-19 pneumonia. OBJECTIVE: The goal of this study was to perform a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms in the automatic stratification of COVID-19 patients using chest images. METHODS: A search strategy for use in PubMed, Scopus, Google Scholar, and Web of Science was developed, where we searched for articles published between January 1 and April 25, 2020. We used the key terms "COVID-19," or "coronavirus," or "SARS-CoV-2," or "novel corona," or "2019-ncov," and "deep learning," or "artificial intelligence," or "automatic detection." Two authors independently extracted data on study characteristics, methods, risk of bias, and outcomes. Any disagreement between them was resolved by consensus. RESULTS: A total of 16 studies were included in the meta-analysis, which included 5896 chest images from COVID-19 patients. The pooled sensitivity and specificity of the DL models in detecting COVID-19 were 0.95 (95% CI 0.94-0.95) and 0.96 (95% CI 0.96-0.97), respectively, with an area under the receiver operating characteristic curve of 0.98. The positive likelihood, negative likelihood, and diagnostic odds ratio were 19.02 (95% CI 12.83-28.19), 0.06 (95% CI 0.04-0.10), and 368.07 (95% CI 162.30-834.75), respectively. The pooled sensitivity and specificity for distinguishing other types of pneumonia from COVID-19 were 0.93 (95% CI 0.92-0.94) and 0.95 (95% CI 0.94-0.95), respectively. The performance of radiologists in detecting COVID-19 was lower than that of the DL models; however, the performance of junior radiologists was improved when they used DL-based prediction tools. CONCLUSIONS: Our study findings show that DL models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of DL-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

6.
Front Med (Lausanne) ; 8: 620044, 2021.
Article in English | MEDLINE | ID: covidwho-1106030

ABSTRACT

Coronavirus disease 2019 (COVID-19) has already raised serious concern globally as the number of confirmed or suspected cases have increased rapidly. Epidemiological studies reported that obesity is associated with a higher rate of mortality in patients with COVID-19. Yet, to our knowledge, there is no comprehensive systematic review and meta-analysis to assess the effects of obesity and mortality among patients with COVID-19. We, therefore, aimed to evaluate the effect of obesity, associated comorbidities, and other factors on the risk of death due to COVID-19. We did a systematic search on PubMed, EMBASE, Google Scholar, Web of Science, and Scopus between January 1, 2020, and August 30, 2020. We followed Cochrane Guidelines to find relevant articles, and two reviewers extracted data from retrieved articles. Disagreement during those stages was resolved by discussion with the main investigator. The random-effects model was used to calculate effect sizes. We included 17 articles with a total of 543,399 patients. Obesity was significantly associated with an increased risk of mortality among patients with COVID-19 (RRadjust: 1.42 (95%CI: 1.24-1.63, p < 0.001). The pooled risk ratio for class I, class II, and class III obesity were 1.27 (95%CI: 1.05-1.54, p = 0.01), 1.56 (95%CI: 1.11-2.19, p < 0.01), and 1.92 (95%CI: 1.50-2.47, p < 0.001), respectively). In subgroup analysis, the pooled risk ratio for the patients with stroke, CPOD, CKD, and diabetes were 1.80 (95%CI: 0.89-3.64, p = 0.10), 1.57 (95%CI: 1.57-1.91, p < 0.001), 1.34 (95%CI: 1.18-1.52, p < 0.001), and 1.19 (1.07-1.32, p = 0.001), respectively. However, patients with obesity who were more than 65 years had a higher risk of mortality (RR: 2.54; 95%CI: 1.62-3.67, p < 0.001). Our study showed that obesity was associated with an increased risk of death from COVID-19, particularly in patients aged more than 65 years. Physicians should aware of these risk factors when dealing with patients with COVID-19 and take early treatment intervention to reduce the mortality of COVID-19 patients.

7.
Front Public Health ; 8: 581746, 2020.
Article in English | MEDLINE | ID: covidwho-976277

ABSTRACT

Purpose: We examined factors associated with health literacy among elders with and without suspected COVID-19 symptoms (S-COVID-19-S). Methods: A cross-sectional study was conducted at outpatient departments of nine hospitals and health centers 14 February-2 March 2020. Self-administered questionnaires were used to assess patient characteristics, health literacy, clinical information, health-related behaviors, and depression. A sample of 928 participants aged 60-85 years were analyzed. Results: The proportion of people with S-COVID-19-S and depression were 48.3 and 13.4%, respectively. The determinants of health literacy in groups with and without S-COVID-19-S were age, gender, education, ability to pay for medication, and social status. In people with S-COVID-19-S, one-score increment of health literacy was associated with 8% higher healthy eating likelihood (odds ratio, OR, 1.08; 95% confidence interval, 95%CI, 1.04, 1.13; p < 0.001), 4% higher physical activity likelihood (OR, 1.04; 95%CI, 1.01, 1.08, p = 0.023), and 9% lower depression likelihood (OR, 0.90; 95%CI, 0.87, 0.94; p < 0.001). These associations were not found in people without S-COVID-19-S. Conclusions: The older people with higher health literacy were less likely to have depression and had healthier behaviors in the group with S-COVD-19-S. Potential health literacy interventions are suggested to promote healthy behaviors and improve mental health outcomes to lessen the pandemic's damage in this age group.


Subject(s)
COVID-19/diagnosis , Depression/diagnosis , Health Behavior , Health Literacy/statistics & numerical data , Outpatients/statistics & numerical data , Aged , Cross-Sectional Studies , Diet, Healthy , Exercise/physiology , Female , Humans , Male , SARS-CoV-2 , Socioeconomic Factors , Surveys and Questionnaires , Vietnam
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