Ontology-driven advanced drug-drug interaction

Abstract The rapid growth of data in the pharmaceutical area has created new challenges for large-scale data mining like Drug-Drug Interaction (DDI) analysis. To meet these challenges, various types of data related to DDI must be integrated with true semantics. However, the existing tools do not provide automated DDI analysis. Interaction details are not machine readable and pharmacists need to do further processing for its extraction. This research paper proposed an ontology-driven Advanced Drug-Drug Interaction (ADDI) system to assists the physicians and pharmacists to identify the DDI effects. ADDI provides ontological definitions and semantic relations among diseases, drugs, ingredients, action mechanism, physiologic effect, dosage formation, administration methods, DDI mechanism, DDI types (Antagonism, Synergism, Potentiation, and Interaction with metabolism), DDI reactions, their frequency and duration. It can be used as Semantic Information Layer (SIL) to resolve the heterogeneity problem and can play a significant role to remove the barriers for semantic interoperability.