Machine Learning Refinement of the NSQIP Risk Calculator: Who Survives the "Hail Mary" Case?

Journal: Journal of the American College of Surgeons
Published Date: Apr 1, 2022

Abstract

BACKGROUND: The American College of Surgeons (ACS) NSQIP risk calculator helps guide operative decision making. In patients with significant surgical risk, it may be unclear whether to proceed with "Hail Mary"-type interventions. To refine predictions, a local interpretable model-agnostic explanations machine (LIME) learning algorithm was explored to determine weighted patient-specific factors' contribution to mortality.

Authors

  • Michael P Rogers
    OnetoMAP Data Analytics and Machine Learning, Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Haroon Janjua
    Department of Surgery, University of South Florida, Tampa, FL; OnetoMap Analytics, University of South Florida, Tampa, FL.
  • Anthony J DeSantis
    OnetoMAP Data Analytics and Machine Learning, Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Emily Grimsley
    From the Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL (Rogers, Janjua, DeSantis, Grimsley, Kuo).
  • Ricardo Pietrobon
    SporeData Inc., Durham, NC (Pietrobon).
  • Paul C Kuo
    Loyola University Medical Center, Department of Surgery, 2160 S. 1st Avenue, Maywood, IL 60153, USA; One:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, 2160 S. 1st Avenue, Maywood, IL 60153, USA. Electronic address: paul.kuo@luhs.org.

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