Healthcare access as an important element for the EU's socioeconomic development: Greece's residents' opinions during the COVID-19 pandemic

Zavras, Dimitris

doi:10.3934/nar.2022020

NAR

2022

DOI: 10.3934/nar.2022020

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Healthcare access as an important element for the EU's socioeconomic development: Greece's residents' opinions during the COVID-19 pandemic

Dimitris Zavras¹

Abstract: <abstract> The coronavirus disease 2019 (COVID-19) pandemic has had a severe impact on global socio-economic development and healthcare access. Considering the link between the two, the objective of this study was to investigate to what extent Greece's residents consider that access to healthcare is an important element for the European's Union (EU) socioeconomic development. The study used data from the Eurobarometer 94.2. Interviews were conducted online. Respondents were recruited by telephon… Show more

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Cited by 5 publications

References 66 publications

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<abstract> The imbalanced data makes the machine learning model seriously biased, which leads to false positive in screening of therapeutic drugs for breast cancer. In order to deal with this problem, a multi-model ensemble framework based on tree-model, linear model and deep-learning model is proposed. Based on the methodology constructed in this study, we screened the 20 most critical molecular descriptors from 729 molecular descriptors of 1974 anti-breast cancer drug candidates and, in order to measure the pharmacokinetic properties and safety of the drug candidates, the screened molecular descriptors were used in this study for subsequent bioactivity, absorption, distribution metabolism, excretion, toxicity, and other prediction tasks. The results show that the method constructed in this study is superior and more stable than the individual models used in the ensemble approach. </abstract>

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Optimal modeling of anti-breast cancer candidate drugs screening based on multi-model ensemble learning with imbalanced data

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Data augmentation based semi-supervised method to improve COVID-19 CT classification

Chen

Bai

Wang

et al. 2023

MBE

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<abstract>The Coronavirus (COVID-19) outbreak of December 2019 has become a serious threat to people around the world, creating a health crisis that infected millions of lives, as well as destroying the global economy. Early detection and diagnosis are essential to prevent further transmission. The detection of COVID-19 computed tomography images is one of the important approaches to rapid diagnosis. Many different branches of deep learning methods have played an important role in this area, including transfer learning, contrastive learning, ensemble strategy, etc. However, these works require a large number of samples of expensive manual labels, so in order to save costs, scholars adopted semi-supervised learning that applies only a few labels to classify COVID-19 CT images. Nevertheless, the existing semi-supervised methods focus primarily on class imbalance and pseudo-label filtering rather than on pseudo-label generation. Accordingly, in this paper, we organized a semi-supervised classification framework based on data augmentation to classify the CT images of COVID-19. We revised the classic teacher-student framework and introduced the popular data augmentation method Mixup, which widened the distribution of high confidence to improve the accuracy of selected pseudo-labels and ultimately obtain a model with better performance. For the COVID-CT dataset, our method makes precision, F1 score, accuracy and specificity 21.04%, 12.95%, 17.13% and 38.29% higher than average values for other methods respectively, For the SARS-COV-2 dataset, these increases were 8.40%, 7.59%, 9.35% and 12.80% respectively. For the Harvard Dataverse dataset, growth was 17.64%, 18.89%, 19.81% and 20.20% respectively. The codes are available at <a href="https://github.com/YutingBai99/COVID-19-SSL" target="_blank">https://github.com/YutingBai99/COVID-19-SSL</a>.</abstract>

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Big data analysis of water quality monitoring results from the Xiang River and an impact analysis of pollution management policies

Zeng

Zhou

Cao

et al. 2023

MBE

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<abstract> Water pollution prevention and control of the Xiang River has become an issue of great concern to China's central and local governments. To further analyze the effects of central and local governmental policies on water pollution prevention and control for the Xiang River, this study performs a big data analysis of 16 water quality parameters from 42 sections of the mainstream and major tributaries of the Xiang River, Hunan Province, China from 2005 to 2016. This study uses an evidential reasoning-based integrated assessment of water quality and principal component analysis, identifying the spatiotemporal changes in the primary pollutants of the Xiang River and exploring the correlations between potentially relevant factors. The analysis showed that a series of environmental protection policies implemented by Hunan Province since 2008 have had a significant and targeted impact on annual water quality pollutants in the mainstream and tributaries. In addition, regional industrial structures and management policies also have had a significant impact on regional water quality. The results showed that, when examining the changes in water quality and the effects of pollution control policies, a big data analysis of water quality monitoring results can accurately reveal the detailed relationships between management policies and water quality changes in the Xiang River. Compared with policy impact evaluation methods primarily based on econometric models, such a big data analysis has its own advantages and disadvantages, effectively complementing the traditional methods of policy impact evaluations. Policy impact evaluations based on big data analysis can further improve the level of refined management by governments and provide a more specific and targeted reference for improving water pollution management policies for the Xiang River. </abstract>

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Healthcare access as an important element for the EU's socioeconomic development: Greece's residents' opinions during the COVID-19 pandemic

Cited by 5 publications

References 66 publications

Optimal modeling of anti-breast cancer candidate drugs screening based on multi-model ensemble learning with imbalanced data

Optimal modeling of anti-breast cancer candidate drugs screening based on multi-model ensemble learning with imbalanced data

Data augmentation based semi-supervised method to improve COVID-19 CT classification

Big data analysis of water quality monitoring results from the Xiang River and an impact analysis of pollution management policies

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