Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?

Journal: Clinical orthopaedics and related research
PMID: 32282466

Abstract

BACKGROUND: Machine-learning methods such as the Bayesian belief network, random forest, gradient boosting machine, and decision trees have been used to develop decision-support tools in other clinical settings. Opioid abuse is a problem among civilians and military service members, and it is difficult to anticipate which patients are at risk for prolonged opioid use.

Authors

  • Ashley B Anderson
    A. B. Anderson, J. A. Forsberg, Department of Surgery, Division of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, MD, USA.
  • Clare F Grazal
    C. F. Grazal, Henry Jackson Foundation, Bethesda, MD, USA.
  • George C Balazs
    G. C. Balazs, Naval Medical Center Portsmouth, Portsmouth, VA, USA.
  • Benjamin K Potter
    A. B. Anderson, B. K. Potter, J. F. Dickens, J. A. Forsberg, Department of Surgery, Division of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, MD, USA.
  • Jonathan F Dickens
    A. B. Anderson, B. K. Potter, J. F. Dickens, J. A. Forsberg, Department of Surgery, Division of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, MD, USA.
  • Jonathan A Forsberg
    A. B. Anderson, J. A. Forsberg, Department of Surgery, Division of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, MD, USA.

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