Machine Learning-based Prediction of Postoperative Pancreatic Fistula Following Pancreaticoduodenectomy.

Journal: Annals of surgery
PMID: 37947154

Abstract

OBJECTIVE: The aim of this study was to develop a novel machine learning model to predict clinically relevant postoperative pancreatic fistula (CR-POPF) following pancreaticoduodenectomy (PD).

Authors

  • Arjun Verma
  • Jeffrey Balian
    Cardiovascular Outcomes Research Laboratories, Department of Surgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA.
  • Joseph Hadaya
    Cardiovascular Outcomes Research Laboratories, Department of Surgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA.
  • Alykhan Premji
    Department of Surgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA.
  • Takayuki Shimizu
    Department of Surgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA.
  • Timothy Donahue
    Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Peyman Benharash

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