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.
Rights and Permissions
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 JM, Wang P, Becker W, Yu H. (2018). Detecting Opioid-Related Aberrant Behavior using Natural Language Processing. Open Access Publications by UMass Chan Authors. Retrieved from https://escholarship.umassmed.edu/oapubs/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