Smart Management of Healthcare Professionals Involved in COVID-19 Contrast With SWAPS

Bellavista, Paolo; Torello, Marco; Corradi, Antonio

doi:10.3389/frsc.2021.638743

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Hence, an AI health care mechanism is fundamentally required to ght against COVID-19 widespread for rescuing medical setups and para-medical staff. Appreciation goes to IoT [23][24][25][26], the internet of medical things (IoMT) [27,28] for providing strong characteristics (i-e live monitoring, speedy communication, and distant checkups) which incredibly help the health care framework of any nation against widespread of COVID-19 [29]. Moreover, healthcare 5.0 with 5G empowered IoMT conditions can successfully enhance the availability of doctors and Para medical staff to patients in far-ung areas, empowering the COVID-infected patients to monitor their health conditions based on everyday suggestions from doctors.…”

Section: Introductionmentioning

confidence: 99%

Deep Implicit Supervision with Multiscale Feature Mapping for COVID-19 Detection in Chest Radiography Images

Aloraini

Habib

Albattah

et al. 2023

Preprint

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The COVID-19 pandemic is considered one of the major outbreaks all over the world, having a serious impact on human health and state economies. One of the important steps involved in fighting against COVID-19 is the early detection of positive patients and keeping them under observation in special care. Detecting COVID-19 from chest X-ray (CX) images is an efficient way to diagnose patients. Therefore, researchers investigated the specific abnormalities in the CX of COVID-19-positive patients. However, the detection accuracy of these methods is not enough for real-time implementation therefore, we develop an effective and efficient model for COVID-19 detection that obtains a better balance among accuracy, specificity, and sensitivity using three benchmark datasets. In the proposed work, a multiscale feature extraction mechanism is used to capture rich spatial information, which improves the discriminative ability of the model to detect COVID-19. Afterward, an implicit deep supervision mechanism is used to increase the interaction among information flows through dense connections. Lastly, a channel attention module selectively highlights the contribution between different feature maps. The experimental results of our model using three benchmark datasets including CXI, XDC, and CRD, demonstrate that our model surpassed the state-of-the-art approaches by achieving higher accuracy, specificity, and sensitivity.

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Section: Introductionmentioning

confidence: 99%

Deep Implicit Supervision with Multiscale Feature Mapping for COVID-19 Detection in Chest Radiography Images

Aloraini

Habib

Albattah

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, an intelligent AI-driven healthcare system is necessarily needed for combating with COVID-19 pandemic and rescuing hospitals and other medical staff. Thanks to the Internet of Things (23)(24)(25)(26) and Internet of Medical Things (IoMT) (27,28) for offering powerful features (i.e., online monitoring, high-speed communication, and remote checkups) that can greatly assist healthcare system of a country against COVID-19 pandemic (29). Also, Healthcare 5.0 with 5Genabled IoMT environment can effectively improve the accessibility of doctors and nurses to their patients in remote areas, enabling COVID-19 patients to control their health based on daily recommendations from doctors.…”

Section: Introductionmentioning

confidence: 99%

WEENet: An Intelligent System for Diagnosing COVID-19 and Lung Cancer in IoMT Environments

et al. 2022

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The coronavirus disease 2019 (COVID-19) pandemic has caused a major outbreak around the world with severe impact on health, human lives, and economy globally. One of the crucial steps in fighting COVID-19 is the ability to detect infected patients at early stages and put them under special care. Detecting COVID-19 from radiography images using computational medical imaging method is one of the fastest ways to diagnose the patients. However, early detection with significant results is a major challenge, given the limited available medical imaging data and conflicting performance metrics. Therefore, this work aims to develop a novel deep learning-based computationally efficient medical imaging framework for effective modeling and early diagnosis of COVID-19 from chest x-ray and computed tomography images. The proposed work presents “WEENet” by exploiting efficient convolutional neural network to extract high-level features, followed by classification mechanisms for COVID-19 diagnosis in medical image data. The performance of our method is evaluated on three benchmark medical chest x-ray and computed tomography image datasets using eight evaluation metrics including a novel strategy of cross-corpse evaluation as well as robustness evaluation, and the results are surpassing state-of-the-art methods. The outcome of this work can assist the epidemiologists and healthcare authorities in analyzing the infected medical chest x-ray and computed tomography images, management of the COVID-19 pandemic, bridging the early diagnosis, and treatment gap for Internet of Medical Things environments.

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The resilience of the regional ecosystems: Healthcare-service organizations, public agents and communities in the times of COVID-19

Troisi,

De Simone,

Franco

2024

Mecosan

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The aim of this study is to compare regional ecosystems' resilience in terms of their ability to contain the spread of Covid-19. This is carried out through institutional measures as well as through spontaneous community behaviour in order to preserve public health and the institutional actions to strengthen the health-care system in dealing with the pandemic. The discussion is framed through the institutional approach. We develop a twostep method. In the first step, regions have been clustered by considering the kind of response to Covid-19, in terms of formal and informal rules. In the second step a random effect panel regression analysis was performed in order to define the effect of the single response variable to Covid-19 on the resilience index over time. Implications for policy makers are then discussed. The first is that coping with a regional emergency requires taking into account regional specificities. Strictly imitative models risk being inconsistent with the characteristics of the area, resulting in outputs that are not homogeneous. The second involves social capital, which is crucial to the application of the norms. In regions with high social capital there is a sense of solidarity that presents itself in informal rules aimed at reinforcing the formal rules. However, there is no sense of conformity for the sake of conformance to the standard.

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Smart Management of Healthcare Professionals Involved in COVID-19 Contrast With SWAPS

Cited by 4 publications

References 15 publications

Deep Implicit Supervision with Multiscale Feature Mapping for COVID-19 Detection in Chest Radiography Images

Deep Implicit Supervision with Multiscale Feature Mapping for COVID-19 Detection in Chest Radiography Images

WEENet: An Intelligent System for Diagnosing COVID-19 and Lung Cancer in IoMT Environments

The resilience of the regional ecosystems: Healthcare-service organizations, public agents and communities in the times of COVID-19

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