Development of a Machine Learning Model Using Electronic Health Record Data to Identify Antibiotic Use Among Hospitalized Patients.

Journal: JAMA network open
Published Date: Mar 1, 2021

Abstract

IMPORTANCE: Comparisons of antimicrobial use among hospitals are difficult to interpret owing to variations in patient case mix. Risk-adjustment strategies incorporating larger numbers of variables haves been proposed as a method to improve comparisons for antimicrobial stewardship assessments.

Authors

  • Rebekah W Moehring
    Duke Center for Antimicrobial Stewardship and Infection Prevention, Duke University Medical Center, Durham, North Carolina.
  • Matthew Phelan
    Center for Predictive Medicine, Duke Clinical Research Institute, Durham, North Carolina.
  • Eric Lofgren
    Paul G. Allen School for Global Animal Health, Washington State University, Pullman.
  • Alicia Nelson
    Duke Center for Antimicrobial Stewardship and Infection Prevention, Duke University Medical Center, Durham, North Carolina.
  • Elizabeth Dodds Ashley
    Duke Center for Antimicrobial Stewardship and Infection Prevention, Duke University Medical Center, Durham, North Carolina.
  • Deverick J Anderson
    Duke Center for Antimicrobial Stewardship and Infection Prevention, Duke University Medical Center, Durham, North Carolina.
  • Benjamin A Goldstein
    Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina.

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