Predicting operative time for metabolic and bariatric surgery using machine learning models: A retrospective observational study.

ABSTRACT:

Background

Predicting operative time is essential for scheduling surgery and managing the operating room (OR). This study aimed to develop machine learning (ML) models to predict the operative time for metabolic and bariatric surgery (MBS) and to compare each model.

Methods

We used the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program database between 2016 and 2020 to develop ML models, including linear regression, random forest (RF), support vector machine, gradient-boosted tree, and XGBoost model. Patient characteristics and surgical features were included as variables in the model. We used the mean absolute error (MAE), root mean square error (RMSE), and R2 score to evaluate model performance. We identified the ten most important variables in t

SUBMITTER: Kang DW 

PROVIDER: S-EPMC11019972 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature