Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial.

Bibliographic Details
Published in:PLoS Digital Health Vol. 5; no. 9; pp. 1 - 18
Main Authors: Li, Huan, Iscoe, Mark, Lutz, John, Hopper, Carolina Diniz, Fried, Sabrina, Minaya, Josue, King, Caroline Raymond, Reykhart, Olga, Paek, Hyung, Meeker, Daniella, Melnick, Edward R., Taylor, R. Andrew
Format: research tables/charts Journal Article
Published: Public Library of Science 9/3/2026
Online Access:View this record in EBSCOhost
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      dt: 9/3/2026
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      pub: Public Library of Science
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        10.1371/journal.pdig.0001140
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        atl: Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial.
      aug:
        au:
          Li, Huan
          Iscoe, Mark
          Lutz, John
          Hopper, Carolina Diniz
          Fried, Sabrina
          Minaya, Josue
          King, Caroline Raymond
          Reykhart, Olga
          Paek, Hyung
          Meeker, Daniella
          Melnick, Edward R.
          Taylor, R. Andrew
        affil: Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America
      sug:
        subj:
          Substance Use Disorders
          Analgesics, Opioid
          Machine Learning
          Phenotype
          Computer Systems
          Patient Selection
          Electronic Health Records
          Emergency Service
          Buprenorphine Therapeutic Use
          Human
          Male
          Female
          Adult
          Middle Age
          Aged
          Retrospective Design
          Record Review
          Random Forest
          Risk Assessment
          Stratified Random Sample
          ROC Curve
          Confidence Intervals
          Sensitivity and Specificity
          Validation Studies
          Decision Support Systems, Clinical
          Nonexperimental Studies
          Logistic Regression
          Descriptive Statistics
          Funding Source
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Aged: 65+ years
          Male
          Female
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
      ab:
    language: English
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