Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata.

Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research.

Adapting Diabetes Risk Scores for Jordan.

BACKGROUND: Diabetes risk score can be used as a simple non-invasive screening tool for identifying people with high risk of diabetes. This study aimed to assess the predictive power of various risk-scoring systems to predict pre-diabetes and diabetes in Jordanian adults. METHODS: This cross-sectional study was conducted among people attending 54 primary health care centers distributed throughout the 12 governorates of Jordan. Diabetes risk scores using the American Diabetes Association risk score, Canadian risk score, Finland risk score (FINDRISC), British Risk score, German and Australian risk score were calculated for each patient. Fasting blood sugar (FBS) was measured for all participants. RESULTS: This study included 392 participants: 231 patients with normal fasting blood sugar (FBG), 101 patients with pre-diabetes and 60 patients with type 2 diabetes. The FINDRISC, British, and Australian risk scores were strongly inter-correlated and weakly correlated with other systems' risk scores. Moreover, they correlated moderately and significantly with FBS. In contrast, other systems risk scores were associated weekly with FBS. Based on receiving operating characteristics (ROC) analysis and multivariate logistic regression, the FINDRISC risk score was superior to other risk scores to predict high FBS and identify pre-diabetes and diabetes. CONCLUSION: FINDRISC risk score performed the best compared to other risk scores for predicting pre-diabetes, diabetes, and absence of diabetes. We recommend using the FINDRISC risk score assessment in Jordan.