A comparison between two approaches to identify opioid use problems: ICD-9 vs. Text-mining approach

Abdullah H. Alzeer, Jay Patel, Brian Dixon, Josette F. Jones, Matthew Bair

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The aim of the research is to ontologically identify and compare the incidence of opioid use problems (OUPs) among patients on long-term opioid therapy using text mining vs. diagnostic coding approaches.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages455-456
Number of pages2
ISBN (Electronic)9781538653777
DOIs
StatePublished - Jul 24 2018
Event6th IEEE International Conference on Healthcare Informatics, ICHI 2018 - New York, United States
Duration: Jun 4 2018Jun 7 2018

Other

Other6th IEEE International Conference on Healthcare Informatics, ICHI 2018
CountryUnited States
CityNew York
Period6/4/186/7/18

Keywords

  • Abuse
  • Addiction
  • Opioid
  • Opioid Use Problems
  • Text-mining

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Health Informatics

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  • Cite this

    Alzeer, A. H., Patel, J., Dixon, B., Jones, J. F., & Bair, M. (2018). A comparison between two approaches to identify opioid use problems: ICD-9 vs. Text-mining approach. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018 (pp. 455-456). [8419433] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2018.00100