Os dados do Sistema de

Digital transformation in e-health is a well-known challenge problem reported from several studies and from several dimensions. In addition, it has been verified a gap in the utilization of new technologies as differential tool in the war against the Covid-19 pandemic. In this paper, we present an ongoing research effort which is characterized for supporting a digital transformation gap found in a public primary healthcare system. Therefore, it can be seen as an interesting case study approach to tackle some challenges found in Covid-19. Utilizing smart bands by groups of different type of voluntaries, where vital signals were collected in a digital data fashion and then evaluated in public health unit. A recommendation system (RS) algorithm was also developed to understand users´ behaviors, based upon their vital signals. In addition, we utilized a simulator software to highlight people movement and predictable scenarios of Covid-19 contamination. This last effort provides a visualization on how the proposal could also help in a real ordinary monitoring scenario. Initial results from this research work indicates a differentiated approach to tackle challenges in digital transformation in a public health scenario, especially in a pandemic. In addition, our experiments illustrate that the adoption of some computational technologies require mainly changes on the present behavior, from governments and people, to be successful approaches to individual protection inside public environments.

Os dados do Sistema de Informação sobre Mortalidade (SIM) representam a principal fonte de informações sobre mortalidade no Brasil, embora apresentem com frequência alguma inconsistência. Um dos principais problemas na geração de dados confiáveis sobre mortalidade no Brasil decorre das dificuldades enfrentadas pelos médicos para o correto preenchimento da Declaração de Óbitos (DO), documento padrão de alimentação de dados para o SIM. O artigo apresenta uma ferramenta de apoio para o correto preenchimento da DO, representada por meio de uma ontologia de aplicação desenvolvida com a participação de profissionais de saúde especialistas no domínio de mortalidade. A ferramenta foi disponibilizada aos médicos da Secretaria Municipal de Saúde e Assistência de Belo Horizonte (SMSA-BH) por meio de um site contendo os termos definidos na ontologia e seus relacionamentos, dispostos numa estrutura taxonômica. A solução proposta tem como objetivo principal auxiliar os médicos em sua tarefa de registrar os óbitos ocorridos, a partir do preenchimento correto da DO.

Digital transformation, big data, artificial intelligence and data analytics are probably the most cited computational subjects in the news around the world. Commercial and technical reports (and papers), usually, indicate high level of success in the adoption of these approaches. In contrast, the present war that the world is facing against the Covid-19, it is not common to see many references about these technologies. In this paper, we present a research work which observes approaches on how to effectively adopt these technologies for a public primary healthcare monitoring, based on Internet of Things devices. Partial results from the proposal indicates a differentiated approach to tackle large challenges, similar to these created by the actual Covid-19 pandemic scenario. In addition, our experiments shown that the adoption of these computational topics require a faster digital behavior changes, to the procedures from governments and people, to be successful as environments for individual health enhancement and protection.

Nowadays there are diferent systems to support teaching and learning, each one has their own objectives and using specifics methods to collect educational data. To conciliate data from different sources is a problem of the Learning Analytics (LA) area. To help in this task, this article presents the initial version of Ontology For Learning Analytics (Onto4LA), created with the objective of facilitating the integration of educational data. The main contribuition of the proposed ontology is make possible the integration of different concepts presents on the frameworks of Learning Analytics.