leveraging the semantic web and natural language processing to enhance drug-mechanism knowledge in drug product labels
Multiple studies indicate that drug-drug interactions are a significant source of preventable adverse drug events. Factors contributing to the occurrence of preventable ADEs resulting from DDIs include a lack of knowledge of the patient's concurrent medications and inaccurate or inadequate knowledge of interactions by health care providers. FDA-approved drug product labeling is a major source of information intended to help clinicians prescribe drugs in a safe and effective manner. Unfortunately, drug product labeling has been identified as often lagging behind emerging drug knowledge; especially when it has been several years since a drug has been released to the market. In this paper we report on a novel approach that explores employing Semantic Web technology and natural language processing to identify drug mechanism information that may update or expand upon statements present in product labeling.
semantic web drug product labeling drug-drug interactions mash-up