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Automated Machine Learning Approaches for Surgery Duration Prediction in Orthopaedics.

Rohan Barrowcliff1, Thomas Lovegrove2, Holger Kunz1

  • 1University College London, Institute of Health Informatics.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

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Automated machine learning (AutoML) significantly improves surgical duration prediction accuracy, reducing operating room overruns. This AI approach offers a 46% improvement over traditional surgeon estimates for better healthcare operations.

Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Operations Research

Background:

  • Accurate surgical case duration prediction is crucial for operating room efficiency.
  • Traditional estimation methods have limited accuracy, with mean absolute errors (MAE) of 30-70 minutes.
  • Optimizing theatre utilization requires precise surgical time forecasting.

Purpose of the Study:

  • To evaluate the performance of AutoGluon, an automated machine learning (AutoML) framework, for predicting elective orthopaedic surgery duration.
  • To compare AutoML performance against traditional statistical and machine learning models.
  • To identify key predictors of surgical duration and overrun risk.

Main Methods:

  • Retrospective analysis of 94,502 elective orthopaedic procedures.
Keywords:
Automated Machine LearningOperating TheatreOrthopaedicsSurgery Duration Prediction

Related Experiment Videos

  • Utilized AutoGluon framework for surgery duration prediction.
  • Compared AutoGluon against linear regression, XGBoost, and a feed-forward neural network using standardized preprocessing and optimization.

    • Main Results:

    • AutoGluon achieved a mean absolute error (MAE) of 15.70 minutes, outperforming XGBoost by 26%.
    • Extended training with AutoGluon reduced MAE to 11.84 minutes, a 46% improvement over surgeon estimates.
    • SHAP analysis identified procedure type, inpatient status, and anesthetic type as key predictors.

    Conclusions:

    • Automated machine learning frameworks like AutoGluon offer state-of-the-art predictive performance for surgical duration.
    • AutoML requires minimal technical expertise, facilitating its adoption in healthcare operations.
    • Improved prediction accuracy enhances operating room utilization and reduces costly overruns.