ABSTRACT
On the 21st century, the exponential growth of technology, led the world facing a myriad of information coming from multitudinous sources. Then, finding ways of storing knowledge committed to certain rules became imperious. Ontologies have been playing an important role on connecting data to the semantics of the real world. Data, without such ontological commitment, could be interpreted as representations of different entities than the one it actually is, leading to biased analysis and inaccurate prediction on data-driven projects. Such kind of artifact formalizes shared knowledge regarding a domain of discourse. Therefore, this study will, based on works showing the benefits of bringing ontologies to the scenario of Machine Learning techniques, enrich similarity metrics between instances of data. So, the Human Disease Ontology (DO) will be used. Instead of calculating pairwise similarities between two diseases (terms on DO), groups of diseases will be considered. Therefore, this work will rely on adapting a groupwise similarity metric Data collection will be done considering the SIVEP-Gripe Dataset. Then, an analysis will be made on how better Machine Learning Algorithms can perform the analysis is made considering semantic rather than just numerical and categorical features. © 2022 Copyright for this paper by its authors.
ABSTRACT
The relevance of foundational ontologies and well-founded conceptual models is acknowledged in several contexts for making the real-world semantics of data explicit, and for reducing the impact of semantic ambiguities during the integration and manipulation of different data sources. Few domains pose such demands as urgently as the analysis and knowledge extraction from COVID-19 data. Since COVID-19 was declared a pandemic in early 2020, huge efforts from around the world provided an avalanche of data for research and analysis at an unprecedented rate. However, the coexistence of semantically divergent and non-explicit definitions for data from distinct countries and time periods that are being integrated and analyzed makes the conclusions of such analysis and the extracted knowledge potentially questionable. This work contributes to the development of a preliminary version of OntoCOVID, an ontology for the domain of COVID-19 well-founded in UFO and built using the SABiO methodology. © 2021 Copyright for this paper by its authors.
ABSTRACT
The COVID-19 pandemic posed several research opportunities raised by the huge amount of data collected and made available at an unprecedented rate. However, this also raised important challenges for manipulating all this data and extracting knowledge from it, due to the lack of semantically precise definitions. In particular, several locations imposed lockdown measures for some periods, with different meanings among them. These semantic differences might help to explain why different countries - which at first sight enforced similar sets of interventions - evolved in a completely distinct way with respect to the propagation rate of COVID-19. In this work, we report an ontological analysis of some lockdown interventions. These interventions are classified in the same category in a taxonomy provided by a worldwide initiative that tracks information on interventions from governments of several countries taken to tackle COVID-19. However, as our analysis shows, there are important ontological distinctions among them. Based on these results, we propose an initial version of a domain ontology that represents lockdown as a complex non-pharmaceutical intervention type, which is composed of interventions of several natures, and that provides a legal perspective of some of its composing interventions using patterns from the UFO-L legal core ontology. © 2021 Copyright for this paper by its authors.
ABSTRACT
OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has highlighted inequalities in access to healthcare systems, increasing racial disparities and worsening health outcomes in these populations. This study analysed the association between sociodemographic characteristics and COVID-19 in-hospital mortality in Brazil. STUDY DESIGN: A retrospective analysis was conducted on quantitative reverse transcription polymerase chain reaction-confirmed hospitalised adult patients with COVID-19 with a defined outcome (i.e. hospital discharge or death) in Brazil. Data were retrieved from the national surveillance system database (SIVEP-Gripe) between February 16 and August 8, 2020. METHODS: Clinical characteristics, sociodemographic variables, use of hospital resources and outcomes of hospitalised adult patients with COVID-19, stratified by self-reported race, were investigated. The primary outcome was in-hospital mortality. The association between self-reported race and in-hospital mortality, after adjusting for clinical characteristics and comorbidities, was evaluated using a logistic regression model. RESULTS: During the study period, Brazil had 3,018,397 confirmed COVID-19 cases and 100,648 deaths. The study population included 228,196 COVID-19-positive adult in-hospital patients with a defined outcome; the median age was 61 years, 57% were men, 35% (79,914) self-reported as Black/Brown and 35.4% (80,853) self-reported as White. The total in-hospital mortality was 37% (85,171/228,196). Black/Brown patients showed higher in-hospital mortality than White patients (42% vs 37%, respectively), were admitted less frequently to the intensive care unit (ICU) (32% vs 36%, respectively) and used more invasive mechanical ventilation (21% vs 19%, respectively), especially outside the ICU (17% vs 11%, respectively). Black/Brown race was independently associated with high in-hospital mortality after adjusting for sex, age, level of education, region of residence and comorbidities (odds ratio = 1.15; 95% confidence interval = 1.09-1.22). CONCLUSIONS: Among hospitalised Brazilian adults with COVID-19, Black/Brown patients showed higher in-hospital mortality, less frequently used hospital resources and had potentially more severe conditions than White patients. Racial disparities in health outcomes and access to health care highlight the need to actively implement strategies to reduce inequities caused by the wider health determinants, ultimately leading to a sustainable change in the health system.