An explainable machine learning framework for predicting the risk of buprenorphine treatment discontinuation for opioid use disorder among commercially insured individuals.

Journal: Computers in biology and medicine
PMID:

Abstract

OBJECTIVES: Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation.

Authors

  • Jabed Al Faysal
    Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA.
  • Md Noor-E-Alam
    Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA.
  • Gary J Young
    Center for Health Policy and Healthcare Research, Northeastern University, Boston, MA, USA; Bouve College of Health Sciences, Northeastern University, Boston, MA, USA; D'Amore-McKim School of Business, Northeastern University, Boston, MA, USA.
  • 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.
  • Amie J Goodin
    Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA.
  • James L Huang
    Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville.
  • Debbie L Wilson
    Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, FL, United States of America.
  • Tae Woo Park
    Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
  • Md Mahmudul Hasan
    Queensland University of Technology (QUT), Centre for Accident Research and Road Safety Queensland (CARRS-Q), Australia; Queensland University of Technology (QUT), Institute of Health and Biomedical Innovation (IHBI), Australia. Electronic address: mahmudul.hasan.eee.kuet@gmail.com.