Constructing Metrics for Evaluating Multi-Relational Association Rules in the Semantic Web from Metrics for Scoring Association Rules

International audienceWe propose a method to construct asymmetric metrics for evaluating the quality of multi-relational association rules coded in the form of SWRL rules. These metrics are derived from metrics for scoring association rules. We use each constructed metric as a fitness function for evolutionary inductive programming employed to discover hidden knowledge patterns (represented in SWRL) from assertional data of ontological knowledge bases. This new knowledge can be integrated easily within the ontology to enrich it. In addition, we also carry out a search for the best metric to score candidate multi-relational association rules in the evolutionary approach by experiment. We performed experiments on three publicly available ontologies validating the performances of our approach and comparing them with the main state-of-the-art systems

Associations of Underlying Health Conditions With Anxiety and Depression Among Outpatients: Modification Effects of Suspected COVID-19 Symptoms, Health-Related and Preventive Behaviors

Objectives: We explored the association of underlying health conditions (UHC) with depression and anxiety, and examined the modification effects of suspected COVID-19 symptoms (S-COVID-19-S), health-related behaviors (HB), and preventive behaviors (PB).Methods: A cross-sectional study was conducted on 8,291 outpatients aged 18–85 years, in 18 hospitals and health centers across Vietnam from 14th February to May 31, 2020. We collected the data regarding participant's characteristics, UHC, HB, PB, depression, and anxiety.Results: People with UHC had higher odds of depression (OR = 2.11; p < 0.001) and anxiety (OR = 2.86; p < 0.001) than those without UHC. The odds of depression and anxiety were significantly higher for those with UHC and S-COVID-19-S (p < 0.001); and were significantly lower for those had UHC and interacted with “unchanged/more” physical activity (p < 0.001), or “unchanged/more” drinking (p < 0.001 for only anxiety), or “unchanged/healthier” eating (p < 0.001), and high PB score (p < 0.001), as compared to those without UHC and without S-COVID-19-S, “never/stopped/less” physical activity, drinking, “less healthy” eating, and low PB score, respectively.Conclusion: S-COVID-19-S worsen psychological health in patients with UHC. Physical activity, drinking, healthier eating, and high PB score were protective factors

Constructing Metrics for Evaluating Multi-Relational Association Rules in the Semantic Web from Metrics for Scoring Association Rules

International audienceWe propose a method to construct asymmetric metrics for evaluating the quality of multi-relational association rules coded in the form of SWRL rules. These metrics are derived from metrics for scoring association rules. We use each constructed metric as a fitness function for evolutionary inductive programming employed to discover hidden knowledge patterns (represented in SWRL) from assertional data of ontological knowledge bases. This new knowledge can be integrated easily within the ontology to enrich it. In addition, we also carry out a search for the best metric to score candidate multi-relational association rules in the evolutionary approach by experiment. We performed experiments on three publicly available ontologies validating the performances of our approach and comparing them with the main state-of-the-art systems

Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-relational Association Rules in the Semantic Web

International audienceWe carry out a comparison of popular asymmetric metrics, originally proposed for scoring association rules, as building blocks for a fitness function for evolutionary inductive programming. In particular, we use them to score candidate multi-relational association rules in an evolutionary approach to the enrichment of populated knowledge bases in the context of the Semantic Web. The evolutionary algorithm searches for hidden knowledge patterns, in the form of SWRL rules, in assertional data, while exploiting the deductive capabilities of ontologies. Our methodology is to compare the number of generated rules and total predictions when the metrics are used to compute the fitness function of the evolutionary algorithm. This comparison, which has been carried out on three publicly available ontologies, is a crucial step towards the selection of suitable metrics to score multi-relational association rules that are generated from ontologies

Continuous versus intermittent endotracheal cuff pressure control for the prevention of ventilator-associated respiratory infections in Vietnam: study protocol for a randomised controlled trial

Contains fulltext : 190869.pdf (publisher's version ) (Open Access

Table_1_Fear of COVID-19, healthy eating behaviors, and health-related behavior changes as associated with anxiety and depression among medical students: An online survey.DOCX

BackgroundMedical students' health and wellbeing are highly concerned during the COVID-19 pandemic. This study examined the impacts of fear of COVID-19 (FCoV-19S), healthy eating behavior, and health-related behavior changes on anxiety and depression.MethodsWe conducted an online survey at 8 medical universities in Vietnam from 7th April to 31st May 2020. Data of 5,765 medical students were collected regarding demographic characteristics, FCoV-19S, health-related behaviors, healthy eating score (HES), anxiety, and depression. Logistic regression analyses were used to explore associations.ResultsA lower likelihood of anxiety and depression were found in students with a higher HES score (OR = 0.98; 95%CI = 0.96, 0.99; p = 0.042; OR = 0.98; 95%CI = 0.96, 0.99; p = 0.021), and in those unchanged or more physical activities during the pandemic (OR = 0.54; 95%CI = 0.44, 0.66; p ConclusionsDuring the pandemic, FCoV-19S and cigarette smoking had adverse impacts on medical students' psychological health. Conversely, staying physically active and having healthy eating behaviors could potentially prevent medical students from anxiety and depressive symptoms.</p