Detecting Opioid-Related Aberrant Behavior using Natural Language Processing
| dc.contributor.author | Lingeman, Jesse M. | |
| dc.contributor.author | Wang, Priscilla | |
| dc.contributor.author | Becker, William | |
| dc.contributor.author | Yu, Hong | |
| dc.date | 2022-08-11T08:09:50.000 | |
| dc.date.accessioned | 2022-08-23T16:45:15Z | |
| dc.date.available | 2022-08-23T16:45:15Z | |
| dc.date.issued | 2018-04-16 | |
| dc.date.submitted | 2018-06-20 | |
| dc.identifier.citation | <p>AMIA Annu Symp Proc. 2018 Apr 16;2017:1179-1185. eCollection 2017.</p> | |
| dc.identifier.issn | 1559-4076 (Linking) | |
| dc.identifier.pmid | 29854186 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14038/40646 | |
| dc.description.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 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. | |
| dc.language.iso | en_US | |
| dc.relation | <p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=29854186&dopt=Abstract">Link to Article in PubMed</a></p> | |
| dc.relation.url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977697/ | |
| dc.rights | 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. | |
| dc.subject | Artificial Intelligence and Robotics | |
| dc.subject | Behavior and Behavior Mechanisms | |
| dc.subject | Health Information Technology | |
| dc.subject | Health Services Research | |
| dc.subject | Library and Information Science | |
| dc.subject | Substance Abuse and Addiction | |
| dc.title | Detecting Opioid-Related Aberrant Behavior using Natural Language Processing | |
| dc.type | Conference Paper | |
| dc.source.volume | 2017 | |
| dc.identifier.legacyfulltext | https://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=4460&context=oapubs&unstamped=1 | |
| dc.identifier.legacycoverpage | https://escholarship.umassmed.edu/oapubs/3449 | |
| dc.identifier.contextkey | 12344365 | |
| refterms.dateFOA | 2022-08-23T16:45:15Z | |
| html.description.abstract | <p>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.</p> | |
| dc.identifier.submissionpath | oapubs/3449 | |
| dc.contributor.department | Department of Quantitative Health Sciences | |
| dc.source.pages | 1179-1185 |
