Literature DB >> 29854186

Detecting Opioid-Related Aberrant Behavior using Natural Language Processing.

Jesse M Lingeman1, Priscilla Wang2, William Becker2,3, Hong Yu4.   

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.

Entities:  

Mesh：

Substances：

Year:  2018        PMID： 29854186      PMCID： PMC5977697     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN： 1559-4076


  6 in total

1.  Ending the Opioid Epidemic - A Call to Action.

Authors:  Vivek H Murthy
Journal:  N Engl J Med       Date:  2016-11-09       Impact factor: 91.245

2.  Aberrant drug-related behaviors: a qualitative analysis of medical record documentation in patients referred to an HIV/chronic pain clinic.

Authors:  Jessica S Merlin; Janet M Turan; Ivan Herbey; Andrew O Westfall; Joanna L Starrels; Stefan G Kertesz; Michael S Saag; Christine S Ritchie
Journal:  Pain Med       Date:  2014-08-19       Impact factor: 3.750

3.  Characteristics of opioid prescriptions in 2009.

Authors:  Nora D Volkow; Thomas A McLellan; Jessica H Cotto; Meena Karithanom; Susan R B Weiss
Journal:  JAMA       Date:  2011-04-06       Impact factor: 56.272

4.  Using natural language processing to identify problem usage of prescription opioids.

Authors:  David S Carrell; David Cronkite; Roy E Palmer; Kathleen Saunders; David E Gross; Elizabeth T Masters; Timothy R Hylan; Michael Von Korff
Journal:  Int J Med Inform       Date:  2015-09-25       Impact factor: 4.046

5.  Enhancing Risk Assessment in Patients Receiving Chronic Opioid Analgesic Therapy Using Natural Language Processing.

Authors:  Irina V Haller; Colleen M Renier; Mitch Juusola; Paul Hitz; William Steffen; Michael J Asmus; Terri Craig; Jack Mardekian; Elizabeth T Masters; Thomas E Elliott
Journal:  Pain Med       Date:  2017-10-01       Impact factor: 3.750

6.  Automated de-identification of free-text medical records.

Authors:  Ishna Neamatullah; Margaret M Douglass; Li-wei H Lehman; Andrew Reisner; Mauricio Villarroel; William J Long; Peter Szolovits; George B Moody; Roger G Mark; Gari D Clifford
Journal:  BMC Med Inform Decis Mak       Date:  2008-07-24       Impact factor: 2.796
  6 in total
  9 in total

1.  Social Media Based Analysis of Opioid Epidemic Using Reddit.

Authors:  Sheetal Pandrekar; Xin Chen; Gaurav Gopalkrishna; Avi Srivastava; Mary Saltz; Joel Saltz; Fusheng Wang
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

2.  Development and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies.

Authors:  Majid Afshar; Dmitriy Dligach; Brihat Sharma; Xiaoyuan Cai; Jason Boyda; Steven Birch; Daniel Valdez; Suzan Zelisko; Cara Joyce; François Modave; Ron Price
Journal:  J Am Med Inform Assoc       Date:  2019-11-01       Impact factor: 4.497

3.  Natural Language Processing and Machine Learning to Identify People Who Inject Drugs in Electronic Health Records.

Authors:  David Goodman-Meza; Amber Tang; Babak Aryanfar; Sergio Vazquez; Adam J Gordon; Michihiko Goto; Matthew Bidwell Goetz; Steven Shoptaw; Alex A T Bui
Journal:  Open Forum Infect Dis       Date:  2022-09-12       Impact factor: 4.423

4.  Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing.

Authors:  Melissa N Poulsen; Philip J Freda; Vanessa Troiani; Anahita Davoudi; Danielle L Mowery
Journal:  Front Public Health       Date:  2022-05-09

5.  Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder.

Authors:  Suzanne V Blackley; Erin MacPhaul; Bianca Martin; Wenyu Song; Joji Suzuki; Li Zhou
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

Review 6.  Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.

Authors:  Barbara M Decker; Chloé E Hill; Steven N Baldassano; Pouya Khankhanian
Journal:  Seizure       Date:  2021-01-13       Impact factor: 3.184

7.  Extracting social determinants of health from electronic health records using natural language processing: a systematic review.

Authors:  Braja G Patra; Mohit M Sharma; Veer Vekaria; Prakash Adekkanattu; Olga V Patterson; Benjamin Glicksberg; Lauren A Lepow; Euijung Ryu; Joanna M Biernacka; Al'ona Furmanchuk; Thomas J George; William Hogan; Yonghui Wu; Xi Yang; Jiang Bian; Myrna Weissman; Priya Wickramaratne; J John Mann; Mark Olfson; Thomas R Campion; Mark Weiner; Jyotishman Pathak
Journal:  J Am Med Inform Assoc       Date:  2021-11-25       Impact factor: 7.942

8.  Development and Validation of Machine Models Using Natural Language Processing to Classify Substances Involved in Overdose Deaths.

Authors:  David Goodman-Meza; Chelsea L Shover; Jesus A Medina; Amber B Tang; Steven Shoptaw; Alex A T Bui
Journal:  JAMA Netw Open       Date:  2022-08-01

9.  Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.

Authors:  Brihat Sharma; Dmitriy Dligach; Kristin Swope; Elizabeth Salisbury-Afshar; Niranjan S Karnik; Cara Joyce; Majid Afshar
Journal:  BMC Med Inform Decis Mak       Date:  2020-04-29       Impact factor: 3.298
  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.