Health Informatics on Big COVID-19 Pandemic Data via N-Shot Learning

Nowadays, very large amounts of data are generating at a fast rate from a wide variety of rich data sources. Valuable information and knowledge embedded in these big data can be discovered by data science, data mining and machine learning techniques. Biomedical records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 635 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 3 years since COVID-19 has declared as a pandemic. Hence, effective strategies, solutions, tools and methods - such as artificial intelligence (AI) and/or big data approaches - to tackle the COVID-19 pandemic and possible future pandemics are in demand. In this paper, we present models to analyze big COVID-19 pandemic data and make predictions via N-shot learning. Specifically, our binary model predicts whether patients are COVID-19 or not. If so, the model predicts whether they require hospitalization or not, whereas our multi-class model predicts severity and thus the corresponding levels of hospitalization required by the patients. Our models uses N-shot learning with autoencoders. Evaluation results on real-life pandemic data demonstrate the practicality of our models towards effective allocation of resources (e.g., hospital facilities, staff). These showcase the benefits of AI and/or big data approaches in tackling the pandemic. © 2022 IEEE.

Analysis of Depression, Anxiety, and Stress Chaos Among Children and Adolescents Using Machine Learning Algorithms

In recent times, the pandemic seems to have a serious impact on the mental health of people around the world across all age groups. This has been manifested in the form of unstable mental conditions, depression, anxiety, stress, and many other similar mental illnesses among individuals. In this study, we explore the use of machine learning classification algorithms to detect and classify children and adolescents with unstable mental conditions such as depression, stress, and anxiety through the Covid-19 period based on demographic information and characteristics using the DASS-21 Scale. Using a dataset of 2050 Chinese participants, an attempt has been made to classify their depression, stress, and anxiety behavior into different levels (Normal, Moderate, and Severe). The classification algorithms considered are Support Vector Machines, KNN, Naive Bayes, and Decision Trees. It is observed that the Support Vector Machine is the most effective method for the classification of mental depression, anxiety, and stress conditions. The goal of the study is to build a classification model for accurate categorization of unknown samples into appropriate psychological chaos levels. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Catalog of pedagogical practices, theories and teaching procedures for the elderly: contributions to the development of m-learning guidelines

This research presents a catalog of pedagogical practices, theories/methods, and teaching procedures for the elderly. Aging is a natural part of life and is a process of progressive and differential degradation. The COVID-19 pandemic highlighted the urgency of care for the elderly public. During the lockdown period, this audience's use of digital technologies grew, especially mobile devices. Despite the increase in the use of these devices, we see that the apps have not been developed considering the particular characteristics of the elderly, since aging can bring challenges and changes related to physical, sensory, perceptual, or cognitive issues. In the context of m-learning apps, it becomes even more urgent to consider pedagogical and accessibility aspects for this audience through specific guidelines for this domain. However, to propose such a guideline, it is necessary to know to define the scope and understand the user's context. Focusing on this context, we could mention AGE Guidelines - a set of m-learning pedagogical and accessibility guidelines for the elderly. This paper aims to discuss some aspects of the experimental and description phases to develop such guidelines: the establishment of a catalog of pedagogical practices, theories/methods, and teaching procedures. To create this catalog, six interviews with teachers specialized in the education of the elderly were carried out. Based on Bardin's method, the interviews were recorded and transcribed for later analysis. As a contribution, we expect that the presented catalog will help construct new educational artifacts, including pedagogical practices, theories/methods, and teaching procedures that effectively enable the teaching and learning process of the elderly in practice. © 2022 IEEE.