An ontology-based safe drug prescription method for controlling diabetes

BackgroundDiabetes is a chronic disease also known as a non-communicable disease that cannot be completely cured but can be controlled through medication, or by maintaining a balanced diet and regular physical activity. Many drugs are available to control diabetes, but these drugs need to be carefully prescribed by the experienced physicians because the drugs may have side effects as well as adverse reactions.ObjectiveWe propose an ontology-based safe drug prescription method for diabetes that assists physicians in prescribing appropriate drugs by considering their side effects and interactions with other drugs, diseases and foods.MethodsThe proposed framework was implemented by creating an ontology by integrating drug-related information which includes side effects, drug interactions with other drugs, diseases and foods from different sources. We developed a Java-based user interface through which information on drug side effects, drug interactions and uses of the drugs can be retrieved using SPARQL queries.ResultsThe inferred results using SWRL rules and SPARQL queries were converted to a questionnaire that was validated by four physicians, and achieved the highest agreement score of 92.8%. The ontology-based model was also evaluated for consistency on HermiT and Pellet reasoners.ConclusionPatients with diabetes frequently have comorbidities requiring multiple medications, which increases the risk of adverse drug reactions. The constructed ontology provides a knowledge base to support physicians in identifying drug side effects and interactions. The Ontology incorporates knowledge related to causes of diabetes including impaired insulin secretion, insulin resistance and other causes based on obesity-related factors.