Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

Journal: Journal of substance use and addiction treatment
PMID: 38852819

Abstract

BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-level factors, and SUD treatment engagement.

Authors

  • David Eddie
    Recovery Research Institute, Center for Addiction Medicine, Massachusetts General Hospital, USA; Department of Psychiatry, Harvard Medical School, USA. Electronic address: deddie@mgh.harvard.edu.
  • John Prindle
    Suzanne Dworak-Peck School of Social Work, University of Southern California, USA.
  • Paul Somodi
    Viterbi School of Engineering, Computer Science, University of Southern California, USA.
  • Isaac Gerstmann
    Viterbi School of Engineering, Computer Science, University of Southern California, USA.
  • Bistra Dilkina
    Viterbi School of Engineering, Computer Science, University of Southern California, USA.
  • Shaddy K Saba
    Suzanne Dworak-Peck School of Social Work, University of Southern California, USA.
  • Graham DiGuiseppi
    Suzanne Dworak-Peck School of Social Work, University of Southern California, USA.
  • Michael Dennis
    Lighthouse Institute, Chestnut Health Systems, Normal, IL, USA.
  • Jordan P Davis
    RAND Corporation, USA.

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