Drug Side Effects Data Representation and Full Spectrum Inferencing Using Knowledge Graphs in Intelligent Telehealth

Drug side effects data contains important constraints about side-effects and conflict avoidance of component and compound drug. These are critically important in checking out prescriptions to avoid complications. Current drug data side effect representations in XML does not have a proper knowledge representation mechanism to clearly specify all kinds of dependencies among the drug components and drugs. Therefore Doctors and caregivers often rely on human interpretation to check prescriptions which can be error-prone. The recently introduced Web Ontology Language (OWL) based approach for medical drug side effects data representation still suffers from several shortcomings inherent to the OWL restrictions like using "is-a" relationship and usage of object property based workarounds losing the clarity and dynamic relationship building expected by domain experts to represent knowledge. The proposed model Drug-Side Effects Representation And Inferencing (D-SERI) built using Knowledge Graph (KG) and enhanced PaceJena shows that the proposed model allowsthe doctors and caregivers to derive dynamic information about side effects avoiding costly errors caused by human interpretation.