Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions.

Journal: JAMA network open
Published Date:

Abstract

IMPORTANCE: Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk.

Authors

  • Wei-Hsuan Lo-Ciganic
    *Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA †Department of Pharmacy, Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ Departments of ‡Health Policy and Management, Graduate School of Public Health §Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh ∥Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System Departments of ¶Biostatistics, Graduate School of Public Health #Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA **Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA.
  • James L Huang
    Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville.
  • Hao H Zhang
    Department of Mathematics, University of Arizona, Tucson.
  • Jeremy C Weiss
    Carnegie Mellon University, Heinz College, Pittsburgh, Pennsylvania.
  • Yonghui Wu
    Department of Health Outcomes and Biomedical Informatics.
  • C Kent Kwoh
    Division of Rheumatology, Department of Medicine, and the University of Arizona Arthritis Center, University of Arizona, Tucson.
  • Julie M Donohue
  • Gerald Cochran
    Program for Addiction Research, Clinical Care, Knowledge, and Advocacy, Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City.
  • Adam J Gordon
    Program for Addiction Research, Clinical Care, Knowledge, and Advocacy, Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City.
  • Daniel C Malone
    Department of Pharmacy, Practice and Science, College of Pharmacy, University of Arizona, Tucson.
  • Courtney C Kuza
    Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Walid F Gellad