Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients.

Journal: Journal of the American College of Surgeons
PMID: 33705983

Abstract

BACKGROUND: The Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tool is an artificial intelligence-based calculator for the prediction of 30-day outcomes in patients undergoing emergency operations. In this study, we sought to assess the performance of POTTER in the emergency general surgery (EGS) population in particular.

Authors

  • Majed W El Hechi
    Department of Surgery, Massachusetts General Hospital, Boston, MA.
  • Lydia R Maurer
    Department of Surgery, Massachusetts General Hospital, Boston, MA.
  • Jordan Levine
    Interpretable AI, Boston, MA.
  • Daisy Zhuo
    Interpretable AI, Boston, Massachusetts.
  • Mohamad El Moheb
    Division of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, Massachusetts.
  • George C Velmahos
    Massachusetts General Hospital & Harvard Medical School, Boston, MA.
  • Jack Dunn
    Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA.
  • Dimitris Bertsimas
    Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA.
  • Haytham Ma Kaafarani
    Division of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA. Electronic address: hkaafarani@mgh.harvard.edu.

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