Department of Quantitative Health Sciences
Artificial Intelligence and Robotics | Behavior and Behavior Mechanisms | Health Information Technology | Health Services Research | Library and Information Science | Substance Abuse and Addiction
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.
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Copyright ©2017 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose.
AMIA Annu Symp Proc. 2018 Apr 16;2017:1179-1185. eCollection 2017.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Lingeman, Jesse M.; Wang, Priscilla; Becker, William; and Yu, Hong, "Detecting Opioid-Related Aberrant Behavior using Natural Language Processing" (2018). Open Access Articles. 3449.
Artificial Intelligence and Robotics Commons, Behavior and Behavior Mechanisms Commons, Health Information Technology Commons, Health Services Research Commons, Library and Information Science Commons, Substance Abuse and Addiction Commons