Leveraging machine learning for duration of surgery prediction in knee and hip arthroplasty - a development and validation study.

ABSTRACT:

Background

Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context.

Methods

eXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospit

SUBMITTER: Langenberger B 

PROVIDER: S-EPMC11877953 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature