Drug Knowledge Expressed as Computable Semantic Triples

摘要

The majority of questions that arise in the practice of medicine relate to drug information. Additionally, adverse reactions account for as many as 98,00o deaths per year in the United States. Adverse drug reactions account for a significant portion of those errors. Many authors believe that clinical decision support associated with computerized physician order entry has the potential to decrease this adverse drug event rate. This decision support requires knowledge to drive the process. One important and rich source of drug knowledge is the DailyMed product labels. In this project we used computationally extracted SNOMED CT™ codified data associated with each section of each product label as input to a rules engine that created computable assertional knowledge in the form of semantic triples. These are expressed in the form of "Drug" Haslndication "SNOMED CT™ code". The information density of drug labels is deep, broad and quite substantial. By providing a computable form of this information content from drug labels we make these important axioms (facts) more accessible to computer programs designed to support improved care.