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An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and
Rodney Allanigue Gabriel1,2, Bhavya Harjai2, Sierra Simpson2
1Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, San Diego, CA, United States.
JMIR Perioperative Medicine
|January 26, 2023
Summary
Accurately predicting spine surgery duration is crucial for operating room efficiency. Ensemble learning models, particularly XGBoost regression, significantly improve case duration predictions compared to traditional methods.
Area of Science:
- Neurosurgery and Health Informatics
- Data Science in Healthcare
- Operating Room Management
Background:
- Accurate estimation of surgical case duration is vital for operating room efficiency.
- Traditional predictive methods in spine surgery, like statistical models, are often less sophisticated.
- Machine learning has been applied to predict outcomes but not specifically case duration.
Purpose of the Study:
- To evaluate an ensemble learning approach for enhancing the accuracy of scheduled spine surgery durations.
- To compare machine learning models against the institution's current predictive methods.
Main Methods:
- Retrospective analysis of 3189 spine surgery cases over 4 years.
- Comparison of multivariable linear regression, random forest, bagging, and XGBoost models.
- Evaluation using R-squared, RMSE, explained variance, and MAE with k-fold cross-validation.
- Feature importance determined using SHAP (Shapley Additive Explanations) analysis.
Main Results:
- The institution's current method showed poor prediction accuracy (R-squared=0.213).
- XGBoost regression demonstrated superior performance with an R-squared of 0.770, RMSE of 92.95 minutes, and MAE of 44.31 minutes.
- Key predictive features included body mass index, spinal fusions, surgical procedure, and number of spine levels.
Conclusions:
- Ensemble learning, specifically XGBoost regression, significantly enhances the accuracy of spine surgery time estimations.
- This approach offers a more reliable tool for operating room scheduling and efficiency.
