@article{6dded389d63044e684af14a3a1a5b189,
title = "Predicting Prolonged Length of Hospital Stay and Identifying Risk Factors Following Total Ankle Arthroplasty: A Supervised Machine Learning Methodology",
keywords = "3, Ankle arthroplasty, Length of stay, Machine learning, Retrospective comparative study, Risk Assessment, Humans, Middle Aged, Risk Factors, Length of Stay/statistics \& numerical data, Male, Algorithms, Postoperative Complications/epidemiology, Arthroplasty, Replacement, Ankle/adverse effects, Female, Osteoarthritis/surgery, Adult, Aged, Retrospective Studies, Supervised Machine Learning, Databases, Factual",
author = "Tadiwanashe Chirongoma and Andrew Cabrera and Alexander Bouterse and David Chung and Daniel Patton and Anthony Essilfie",
note = "Copyright {\textcopyright} 2024 the American College of Foot and Ankle Surgeons. All rights reserved.",
year = "2024",
month = sep,
day = "1",
doi = "10.1053/j.jfas.2024.05.005",
language = "English",
volume = "63",
pages = "557--561",
journal = "Journal of Foot and Ankle Surgery",
issn = "1067-2516",
number = "5",
}