Identifying high-dose opioid prescription risks using machine learning: A focus on sociodemographic characteristics.

Journal: Journal of opioid management
PMID: 40326727

Abstract

OBJECTIVE: The objective of this study was to leverage machine learning techniques to analyze administrative claims and socioeconomic data, with the aim of identifying and interpreting the risk factors associated with high-dose opioid prescribing.

Authors

  • Olabode B Ogundele
    Institute for Data Science and Informatics (MUIDSI); Missouri Telehealth Network (MTN), University of Missouri, Columbia, Missouri.
  • Butros M Dahu
    Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA.
  • Praveen Rao
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States.
  • Xing Song
    Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Kansas, USA.
  • Timothy Haithcoat
    Institute for Data Science and Informatics (MUIDSI), University of Missouri, Columbia, Missouri.
  • Mutiyat Hameed
    Sisters of St. Mary (SSM) Health, St. Louis, Missouri.
  • Douglas Burgess
    Department of Psychiatry, University of Missouri Kansas City (UMKC), Kansas City, Missouri.
  • Tracy Greever-Rice
    College of Health Sciences, Center for Health Policy, University of Missouri, Columbia, Missouri.
  • Mirna Becevic
    Mirna Becevic, PhD, MHA, is an assistant research professor of telemedicine at the University of Missouri Department of Dermatology in Columbia, MO.

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