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. 2018 Apr 16:2017:1179-1185.
eCollection 2017.

Detecting Opioid-Related Aberrant Behavior using Natural Language Processing

Jesse M Lingeman  1 , Priscilla Wang  2 , William Becker  2   3 , Hong Yu  4
Affiliations

Detecting Opioid-Related Aberrant Behavior using Natural Language Processing

Jesse M Lingeman et al. AMIA Annu Symp Proc. .

Abstract

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 20001. 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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Figures

Figure 1.
Figure 1.
Accuracy over different sets of features. HC are hand-crafted features, S refers to sentiment features, U refers to unigram features, N refers to n-grams, E refers to embeddings.
See this image and copyright information in PMC

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