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Quickly identifying people at risk of opioid use disorder in emergency departments: trade-offs between a machine learning approach and a simple EHR flag strategy.
Annis, Izabela E; Jordan, Robyn; Thomas, Kathleen C.
Affiliation
  • Annis IE; Division of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA izabela@unc.edu.
  • Jordan R; Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA.
  • Thomas KC; Division of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA.
BMJ Open ; 12(9): e059414, 2022 09 14.
Article de En | MEDLINE | ID: mdl-36104124
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Douleur chronique / Troubles liés aux opiacés Type d'étude: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limites: Female / Humans / Male / Middle aged Langue: En Journal: BMJ Open Année: 2022 Type de document: Article Pays d'affiliation: États-Unis d'Amérique
Texte intégral
Ajouter à My VHL
Imprimer
XML
PubMed Links
Recherche sur Google

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Douleur chronique / Troubles liés aux opiacés Type d'étude: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limites: Female / Humans / Male / Middle aged Langue: En Journal: BMJ Open Année: 2022 Type de document: Article Pays d'affiliation: États-Unis d'Amérique