A novel artificial intelligence framework to quantify the impact of clinical compared with nonclinical influences on postoperative length of stay.

Journal: Surgery
PMID: 39891965

Abstract

BACKGROUND: The relative proportion of clinical compared with nonclinical influences on length of stay after colectomy has never been measured. We developed a novel machine-learning framework that quantifies the proportion of length of stay after colectomy attributable to clinical factors and infers the overall impact of nonclinical influences.

Authors

  • Mohamad El Moheb
    Division of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, Massachusetts.
  • Chengli Shen
    Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
  • Susan Kim
    Department of Surgery, University of Virginia, Charlottesville, VA.
  • Kaelyn Cummins
    Department of Surgery, University of Virginia, Charlottesville, VA.
  • Olivia Sears
    Department of Surgery, University of Virginia, Charlottesville, VA.
  • Zeyad Sahli
    Department of Surgery, University of Virginia, Charlottesville, VA.
  • Hongji Zhang
    Department of Surgery, University of Virginia, Charlottesville, VA.
  • Traci Hedrick
    Department of Surgery, University of Virginia, Charlottesville, VA. Electronic address: https://twitter.com/tlhedr0.
  • Russell G Witt
    Department of Surgery, University of Virginia, Charlottesville, VA.
  • Allan Tsung
    Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA tsunga@upmc.edu.

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