ODD: A Benchmark Dataset for the NLP-based Opioid Related Aberrant Behavior Detection

Opioid related aberrant behaviors (ORAB) present novel risk factors for opioid
overdose. Previously, ORAB have been mainly assessed by survey results and by
monitoring drug administrations. Such methods however, cannot scale up and do
not cover the entire spectrum of aberrant behaviors. On the other hand, ORAB
are widely documented in electronic health record notes. This paper introduces
a novel biomedical natural language processing benchmark dataset named ODD,
for ORAB Detection Dataset. ODD is an expert-annotated dataset comprising of
more than 750 publicly available EHR notes. ODD has been designed to identify
ORAB from patients' EHR notes and classify them into nine categories; 1)
Confirmed Aberrant Behavior, 2) Suggested Aberrant Behavior, 3) Opioids, 4)
Indication, 5) Diagnosed opioid dependency, 6) Benzodiapines, 7) Medication
Changes, 8) Central Nervous System-related, and 9) Social Determinants of
Health.