Open AccessMachine Learning Algorithms Predict Prolonged Opioid Use in Opioid-Naïve Primary Hip Arthroscopy Patients
Author(s) -
Kyle N. Kunze,
Evan M. Polce,
Thomas D. Alter,
Shane J. Nho
Publication year - 2021
Publication title -
journal of the american academy of orthopaedic surgeons. global research and reviews
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.358
H-Index - 2
ISSN - 2474-7661
DOI - 10.5435/jaaosglobal-d-21-00093
Subject(s) - brier score , medicine , gradient boosting , hip arthroscopy , machine learning , opioid , algorithm , logistic regression , random forest , artificial intelligence , arthroscopy , surgery , computer science , receptor
Excessive opioid use after orthopaedic surgery procedures remains a concern because it may result in increased morbidity and imposes a financial burden on the healthcare system. The purpose of this study was to develop machine learning algorithms to predict prolonged opioid use after hip arthroscopy in opioid-naïve patients.