Predictors of treatment attrition among individuals in substance use disorder treatment: A machine learning approach.

Journal: Addictive behaviors
PMID: 39889364

Abstract

BACKGROUND: Early treatment discontinuation in substance use disorder treatment settings is common and often difficult to predict. We leveraged a machine learning approach (i.e., random forest) to identify individuals at risk for treatment attrition, and specific factors associated with treatment discontinuation.

Authors

  • Jill A Rabinowitz
    Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA. Electronic address: jill.rabinowitz@rutgers.edu.
  • Jonathan L Wells
    Department of Epidemiology, Virginia Commonwealth University School of Population Health, Richmond, Virginia USA.
  • Geoffrey Kahn
    Center for Health Policy and Health Services Research, Henry Ford Health, Detroit, Michigan USA.
  • Jennifer D Ellis
    Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD USA.
  • Justin C Strickland
    Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD USA; Ashley Addiction Treatment, MD USA.
  • Martin Hochheimer
    Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD USA.
  • Andrew S Huhn
    Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD USA; Ashley Addiction Treatment, MD USA.

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