Benchmarking Ensemble Models to Predict Prolonged Hospital Stay in Traumatic Brain Injury: A Single-Institution Study.

Journal: The Journal of surgical research
Published Date: Jul 5, 2025

Abstract

INTRODUCTION: Prolonged length of stay (PLOS) in hospitals is a critical metric representing quality and efficiency of care, especially for patients with traumatic brain injury (TBI). Machine learning offers the potential to predict PLOS, although class imbalance, limited sample size, or lack of generalizability impact their real-world application. This study benchmarks machine learning models from prior studies and explores ensemble models to predict PLOS in TBI patients and address domain adaptation concerns in surgical settings.

Authors

  • Shrinit Babel
    1Morsani College of Medicine, University of South Florida, Tampa, Florida.
  • Jade Vanderpool
    University Of South Florida Morsani College Of Medicine, Tampa, Florida.
  • Maurice Inkel
    University Of South Florida Morsani College Of Medicine, Tampa, Florida; Division of Acute Care surgery, Tampa General Hospital, Tampa, Florida.
  • Sandra M Farach
    University Of South Florida Morsani College Of Medicine, Tampa, Florida; Division of Acute Care surgery, Tampa General Hospital, Tampa, Florida.
  • Jose J Diaz
    University Of South Florida Morsani College Of Medicine, Tampa, Florida; Division of Acute Care surgery, Tampa General Hospital, Tampa, Florida.
  • Milad Behbahaninia
    University Of South Florida Morsani College Of Medicine, Tampa, Florida; Division of Acute Care surgery, Tampa General Hospital, Tampa, Florida. Electronic address: behbahaninia@usf.edu.

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