Classifying High-Risk Patients for Persistent Opioid Use After Major Spine Surgery: A Machine-Learning Approach
The balanced random forest classifier was found to be the most effective model for identifying persistent opioid use after spine surgery.
Authors
- Michael Armaneous (ORCID: https://orcid.org/0000-0002-3779-2406)
- Soraya Mehdipour (ORCID: https://orcid.org/0000-0002-5132-4297)
- Engy T. Said (ORCID: https://orcid.org/0000-0002-7897-1670)
- William Zhong
- Natalie Walker (ORCID: https://orcid.org/0000-0001-5939-5009)
- Sierra Simpson (ORCID: https://orcid.org/0000-0002-1352-0617)
- Varshini Sathish
- Rodney A. Gabriel (ORCID: https://orcid.org/0000-0003-4443-0021)
Institutions
- Riverside University Health System - Medical Center (US)
- University of California San Diego (US)
Publication Details
- Journal
- Anesthesia & Analgesia
- Published
- 2024-09-04
- DOI
- https://doi.org/10.1213/ane.0000000000006832
- Citations
- 16
- Primary Topic
- Opioid Use Disorder Treatment
- Type
- article
- Field-Weighted Citation Impact
- 8.69
Funders
- SPR Therapeutics