A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic
Maulik R. Kamdar, Tymor Hamamsy, Shea Shelton, Ayin Vala, Tome, Eftimov, James Zou, Suzanne Tamang

TL;DR
This paper introduces a knowledge graph for opioids to normalize clinical data, enabling analysis of prescription trends and aiding efforts to monitor and combat the US opioid epidemic.
Contribution
The creation of the Opioid Drug Knowledge Graph (ODKG) to address semantic heterogeneity in clinical data and facilitate opioid misuse detection.
Findings
Normalized drug data across 400+ healthcare facilities
Generated regional opioid prescription statistics
Supported development of scalable epidemic monitoring models
Abstract
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…
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Data Quality and Management · Semantic Web and Ontologies
