Drug Side Effects Data Representation and Full Spectrum Inferencing using Knowledge Graphs in Intelligent Telehealth
Drug adverse reaction data contains important constraints about side effects and conflict avoidance of component and compound drugs. We observe that many of these constraints are transitive in nature due to the relationship between drug and drug classes. Current drug side effects representations in XML does not have a proper knowledge representation mechanism to clearly specify all kinds of dependencies among the drug components and drugs. Even the recently introduced 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 emulations. ^ In this research, we propose a model Drug - Side Effects Representation And Inferencing (D -SERI) built using Knowledge Graph (KG) and enhanced PaceJena to represent multiple custom relationships allowing domain experts to capture the transitive nature of the relations in an inference friendly way. The research also developed a concept demonstrator for checking out prescriptions to avoid complications. The research outcome shows that the proposed model allows the doctors and caregivers to derive dynamic information about side-effects avoiding costly errors caused by human interpretation. ^
Jayaraman, Saravanan, "Drug Side Effects Data Representation and Full Spectrum Inferencing using Knowledge Graphs in Intelligent Telehealth" (2016). ETD Collection for Pace University. AAI10247923.
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