Abstract
Background2019-nCoV has been spreading around the world and becoming a global concern. To prevent further widespread of 2019-nCoV, confirmed and suspected cases of COVID-19 infection are suggested to be kept in quarantine. However, the diagnose of COVID-19 infection is quite time-consuming and labor-intensive. To alleviate the burden on the medical staff, we have done some research on the intelligent diagnosis of COVID-19.MethodsIn this paper, we constructed a COVID-19 Diagnosis Ontology (CDO) by utilizing Protégé, which includes the basic knowledge graph of COVID-19 as well as diagnostic rules translated from Chinese government documents. Besides, SWRL rules were added into the ontology to infer intimate relationships between people, thus facilitating the efficient diagnosis of the suspected cases of COVID-19 infection. We downloaded real-case data and extracted patients’ syndromes from the descriptive text, so as to verify the accuracy of this experiment.ResultsAfter importing those real instances into Protégé, we demonstrated that the COVID-19 Diagnosis Ontology showed good performances to diagnose cases of COVID-19 infection automatically.ConclusionsIn conclusion, the COVID-19 Diagnosis Ontology will not only significantly reduce the manual input in the diagnosis process of COVID-19, but also uncover hidden cases and help prevent the widespread of this epidemic.
Highlights
With the outbreak of the 2019-nCoV [1], the sharp increase in the number of COVID-19 infection cases has made medical supplies in short
The Friend of a Friend (FOAF) project is the earliest attempt by Libby Miller and Dan Brickley to introduce the Semantic Web into the field of social networks
We created the class “Social_Network” in COVID-19 Diagnosis Ontology (CDO) by enriching the subclass “Person” from FOAF, aiming to derive social interaction and relationships among targeted people, so as to predict the probability of a person getting infected by COVID-19 through interaction with other people
Summary
With the outbreak of the 2019-nCoV [1], the sharp increase in the number of COVID-19 infection cases has made medical supplies in short. Since the causes of COVID-19 are complex and diverse, the determination of suspected or confirmed cases of COVID-19 infection is quite time-consuming and labor-intensive. Wu et al BMC Med Inform Decis Mak (2021) 21:271 diagnosing suspected cases and confirmed cases of COVID-19 infection based on real patient data. A key factor that affects the diagnosis of suspected cases of COVID-19 infection is the interaction between people. We leveraged and expanded the lightweight social network ontology FOAF (Friend of a Friend) [6] towards capturing the intimate contacts between targeted people. Through the expansion of the FOAF ontology and the usage of SWRL rules, The implicit kinship and contacts among people can be inferred, and get a more complete map of the social network around them, make it less likely to misjudge cases of COVID-19 infection
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