ABSTRACT
The World Health Organization (WHO) has declared the novel coronavirus (COVID-19) outbreak a global pandemic in March 2020. Through a lot of cooperation and the effort of scientists, several vaccines have been created. However, there is no guarantee that the virus will shortly disappear, even if a large part of the population is vaccinated. Therefore, non-invasive methods, with low cost and real-time results, are important to detect infected individuals and enable earlier adequate treatment, in addition to preventing the spread of the virus. An alternative is using forced cough sounds and medical information to distinguish a healthy person from those infected with COVID-19 via artificial intelligence. An additional challenge is the unbalancing of these data, as there are more samples of healthy individuals than contaminated ones. We propose here a Deep Neural Network model to classify people as healthy or sick concerning COVID-19. We used here a model composed by an Convolutional Neural Network and two other Neural Networks with two full-connected layers, each one trained with different data from the same individual. To evaluate the performance of the proposed method, we combined two datasets from the literature: COUGHVID and Coswara. That dataset contains clinical information regarding previous respiratory conditions, symptoms (fever or muscle pain), and a cough record. The results show that our model is simpler (with fewer parameters) than those from the literature and generalizes better the prediction of infected individuals. The proposal presents an average Area Under the ROC Curve (AUC) equal to 0.885 with a confidence interval (0.881-0.888), while the literature reports 0.771 with (0.752-0.783). © 2023, Federal University of Rio Grande do Sul, Institute of Informatics. All rights reserved.
ABSTRACT
The SARS-CoV-2/COVID-19 pandemic has caused various changes in the public and private context, raising moral, legal, political and social challenges for the daily life of the society around the world. This paper offers some reflections on the ethics and care for life, in a time when sudden changes were associated with the challenge of survival viability and the importance of relational care in the context of the pandemic. In view of the mandate for social isolation and distancing, protection strategies were sought for a social continuum. In this context, the care framework was built around the notion of salutogenesis and self-preservation, which underlies a value system incorporating knowledge related to health, quality of life and well-being. In a systemic view, through public policies and humanitarian aid, there is the assumption that new principles and values will guide the future experience focusing on the preservation of human life, ethical health care and planetary preservation for the good living of humanity.
ABSTRACT
The impact of the pandemic has put the world on alert not only because of the health crisis, but also because of the economic and political impacts it unleashed. This essay reflects on disadvantaged and marginalized populations, more exposed to covid-19 due to the conditions in which they survive, with reduced access to basic sanitation and, therefore, less likely to adopt the recommended preventive measures. Worldwide, the contingency plan against Sars-CoV-2 required governments to take measures to protect human life and seek health equity. The Brazilian State, however, shirked from prior preparation to face the pandemic and did not establish coordinated strategies. Given that justice and equity in health strengthen cohesion in the search for safeguarding lives, ethics in care should include the implementation of public policies that correct distortions. © 2021, Conselho Federal de Medicina. All rights reserved.