Hierarchical Concept Relations Improve Detection of Off-Label Drug Use in Electronic Health Records Data.

Real-world clinical practice commonly veers from formal drug approvals in off-label use, accounting for 21% of prescriptions for common drugs. Due to its ad hoc nature, off-label use typically goes undocumented, evading the safety and efficacy scrutiny of clinical trials. A systematic and automated approach to detection of these uses in the electronic health record (EHR) would enable improved safety monitoring, provide insight into prescribing patterns, and support real-world evidence appraisal. Domain knowledge provided by medication-indication knowledge bases has been shown to improve the accuracy of EHR-based automated detection of off-label use, but remains limited due to diverse concept representations and granularities across data sources. We present a method to leverage hierarchical concept knowledge to align medication-indication knowledge with EHR data for automated detection of off-label drug use in clinical practice. We demonstrate an over two-fold increase in detected off-label diagnoses when leveraging hierarchical knowledge relative to direct concept matching alone.