Detecting Opioid-Related Aberrant Behavior using Natural Language Processing
UMass Chan Affiliations
Department of Quantitative Health SciencesDocument Type
Conference PaperPublication Date
2018-04-16Keywords
Artificial Intelligence and RoboticsBehavior and Behavior Mechanisms
Health Information Technology
Health Services Research
Library and Information Science
Substance Abuse and Addiction
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The 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.Source
AMIA Annu Symp Proc. 2018 Apr 16;2017:1179-1185. eCollection 2017.