Detecting Opioid-Related Aberrant Behavior using Natural Language Processing
UMass Chan AffiliationsDepartment of Quantitative Health Sciences
Document TypeConference Paper
KeywordsArtificial Intelligence and Robotics
Behavior and Behavior Mechanisms
Health Information Technology
Health Services Research
Library and Information Science
Substance Abuse and Addiction
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AbstractThe United States is in the midst of a prescription opioid epidemic, with the number of yearly opioid-related overdose deaths increasing almost fourfold since 2000(1). To more effectively prevent unintentional opioid overdoses, the medical profession requires robust surveillance tools that can effectively identify at-risk patients. Drug-related aberrant behaviors observed in the clinical context may be important indicators of patients at risk for or actively abusing opioids. In this paper, we describe a natural language processing (NLP) method for automatic surveillance of aberrant behavior in medical notes relying only on the text of the notes. This allows for a robust and generalizable system that can be used for high volume analysis of electronic medical records for potential predictors of opioid abuse.
AMIA Annu Symp Proc. 2018 Apr 16;2017:1179-1185. eCollection 2017.