A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic
This work addresses the opioid epidemic for policymakers and pain researchers by providing tools to systematically assess over-prescribing factors, though it is incremental as it builds on existing knowledge graph methods for a specific domain.
The authors tackled the problem of semantic heterogeneity in clinical data across U.S. healthcare centers by creating the Opioid Drug Knowledge Graph (ODKG) to normalize drug strings from over 400 facilities in 42 states, enabling summary statistics of opioid prescription trends.
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.