The United States is in the midst of an opioid epidemic with recent estimates
indicating that more than 130 people die every day due to drug overdose. The
over-prescription and addiction to opioid painkillers, heroin, and synthetic
opioids, has led to a public health crisis and created a huge social and
economic burden. Statistical learning methods that use data from multiple
clinical centers across the US to detect opioid over-prescribing trends and
predict possible opioid misuse are required. However, the semantic
heterogeneity in the representation of clinical data across different centers
makes the development and evaluation of such methods difficult and non-trivial.
We create the Opioid Drug Knowledge Graph (ODKG) -- a network of opioid-related
drugs, active ingredients, formulations, combinations, and brand names. We use
the ODKG to normalize drug strings in a clinical data warehouse consisting of
patient data from over 400 healthcare facilities in 42 different states. We
showcase the use of ODKG to generate summary statistics of opioid prescription
trends across US regions. These methods and resources can aid the development
of advanced and scalable models to monitor the opioid epidemic and to detect
illicit opioid misuse behavior. Our work is relevant to policymakers and pain
researchers who wish to systematically assess factors that contribute to opioid
over-prescribing and iatrogenic opioid addiction in the US.

本论文描述了使用药物知识图谱（ODKG）来归一化多个临床数据中的药物命名，以监测美国不同地区的阿片类药物处方趋势，并有助于制定先进和可扩展的模型，以监测阿片类药物滥用行为。