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Using natural language processing to identify opioid use disorder in electronic health record data.
Singleton, Jade; Li, Chengxi; Akpunonu, Peter D; Abner, Erin L; Kucharska-Newton, Anna M.
Affiliation
  • Singleton J; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY 40536, United States; University of Kentucky Healthcare IT Department, Business Intelligence, Lexington, KY 40517, United States. Electronic address: jade.singleton@seattlechildrens.org.
  • Li C; Department of Computer Science, College of Engineering, University of Kentucky, Lexington, KY 40526, United States.
  • Akpunonu PD; Emergency Medicine & Medical Toxicology, University of Kentucky Hospital, Lexington, KY 40536, United States.
  • Abner EL; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY 40536, United States.
  • Kucharska-Newton AM; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY 40536, United States; Department of Epidemiology, The Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, United States.
Int J Med Inform ; 170: 104963, 2023 02.
Article in En | MEDLINE | ID: mdl-36521420
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Natural Language Processing / Opioid-Related Disorders Type of study: Diagnostic_studies / Guideline / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Int J Med Inform Journal subject: INFORMATICA MEDICA Year: 2023 Document type: Article Country of publication:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Natural Language Processing / Opioid-Related Disorders Type of study: Diagnostic_studies / Guideline / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Int J Med Inform Journal subject: INFORMATICA MEDICA Year: 2023 Document type: Article Country of publication: