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Opioid related aberrant behaviors (ORABs) present novel risk factors foropioid overdose. This paper introduces a novel biomedical natural languageprocessing benchmark dataset named ODD, for ORAB Detection Dataset. ODD is anexpert-annotated dataset designed to identify ORABs from patients' EHR notesand classify them into nine categories; 1) Confirmed Aberrant Behavior, 2)Suggested Aberrant Behavior, 3) Opioids, 4) Indication, 5) Diagnosed opioiddependency, 6) Benzodiazepines, 7) Medication Changes, 8) Central NervousSystem-related, and 9) Social Determinants of Health. We explored twostate-of-the-art natural language processing models (fine-tuning andprompt-tuning approaches) to identify ORAB. Experimental results show that theprompt-tuning models outperformed the fine-tuning models in most categories andthe gains were especially higher among uncommon categories (Suggested AberrantBehavior, Confirmed Aberrant Behaviors, Diagnosed Opioid Dependence, andMedication Change). Although the best model achieved the highest 88.17% onmacro average area under precision recall curve, uncommon classes still have alarge room for performance improvement. ODD is publicly available.

ODD: A Benchmark Dataset for the Natural Language Processing based Opioid Related Aberrant Behavior Detection