Recommendation system for emotional self-control of older adults post-COVID-19 in Panama

The effects of the pandemic can translate into a variety of physical and emotional reactions that are affecting the population, particularly the elderly Panamanian population, who have not been able to overcome the mainly emerging challenges of an infectious disease with health implications. physical and has also profoundly affected their well-being and mental health. To allow the Panamanian elderly population to improve emotional self-control and mental relaxation, we propose a software architecture for the development of a recommendation system integrating: artificial intelligence (AI), internet of things (IoT) and mobile applications. This research will contribute to the elderly population in Panama having a mobile application which is beneficial as a non-pharmaceutical alternative to cope with psychological conditions caused by the Covid-19 disease. Regarding the most relevant limitations we have are the acquisition of the data set for training. As future works, we hope to have a more robust architecture to implement it in other activities related to the heath self-control of Panamanian patients. © 2022, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Study on technologies implemented in the area of mental health for older adults Post-COVID-19

Mental health has many alterations that are known as mental illnesses or disorders, which affect affective and cognitive processes, mood, thinking and behavior of human beings. Many people suffer from mental health conditions on some occasions. On the other hand, it becomes a mental illness when the symptoms and signs remain, causing stress in the individual, preventing him/her from developing in a natural way. Because of this, this paper proposes an analysis of different information and communication technology (ICT) architectures developed around mental health with the purpose of facilitating the greatest possible help. © 2022 IEEE.

AFFECTIVE CHANGES IN CHILDREN AND ADOLESCENTS: BEFORE AND DURING PREVENTIVE LOCKDOWN BY COVID-19

Objective: To analyze affective changes in children and adolescents between 8 and 16 years of age, before and during COVID-19 lockdown. Materials and Methods: Observational, cross-sectional study with repeated evoked measurement. Under non-probabilistic sampling, 87 voluntary school children and adolescents were surveyed online using the positive/negative affect questionnaire for children, with the addition of sociodemographic questions. Descriptive/associative analysis was performed to determine the behavior of child and adolescent affectivity before and after lockdown. Results: A significant decrease in positive affect during lockdown was observed (Z =-3.073;p=.002), as well as a significant increase in the probability that the previous affective state, positive (OR: 32.1: 95% CI 8.1-127.2) or negative (OR: 10.8: 95% CI 3.9-29.4), would increase the deterioration of present affectivity. Finally, there were no significant changes in negative affectivity before and during lockdown. Conclusions: During lockdown, a deterioration in the perception of emotional well-being associated with a decrease in positive affect was observed, with previous affectivity being a risk factor for the deterioration of well-being perceived by children and adolescents. It is necessary to strengthen the coping capacity of children so that positive affect becomes a protective factor in the face of new catastrophes. © 2022, Universidad de Concepcion. All rights reserved.

Analysis of Emerging Technologies Proposed to Reduce the Impact of the COVID-19 Pandemic: A Review

The pandemic caused by COVID-19 has changed the way people live, forcing them to adopt measures to avoid transmission, so much so that countries have had to develop containment strategies because this virus continues to spread throughout the planet. It is important to define strategies that support physicians to prevent and improve the incidence of cases and thus avoid the collapse of health systems. The implementation of different technologies is convenient because it allows monitoring and prevention to be done more quickly. This paper analyzes emerging technologies such as mobile applications, devices (IoT) and artificial intelligence models as alternatives to traditional processes. © 2022 IEEE.

Deep learning and machine learning approach applied to the automatic classification of opinions on Twitter in the Covid-19 pandemic in Panama

Twitter is an important social network and information channel where opinions (tweets) can be obtained and processed in real time that can be explored, analyzed and organized to make better decisions. Opinion mining is a natural language processing task that identifies user opinions as positive, negative, or neutral. COVID-19 is an infectious disease caused by the coronavirus that appeared in December 2019 in China and immediately provoked a large number of opinions. To allow Panamanian health organizations to detect opportunities to improve the quality of medical care, we propose to classify the tweets the analysis of two approaches: deep learning and machine learning for to appreciate which is more precise. We obtained encouraging results with a precision of 95.6%. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.