Developing a Novel Artificial Intelligence Framework to Measure the Balance of Clinical Versus Nonclinical Influences on Posthepatectomy Length of Stay.

Journal: Annals of surgical oncology
PMID: 39907879

Abstract

BACKGROUND: Length of stay (LOS) is a key indicator of posthepatectomy care quality. While clinical factors influencing LOS are identified, the balance between clinical and nonclinical influences remains unquantified. We developed an artificial intelligence (AI) framework to quantify clinical influences on LOS and infer the impact of hard-to-measure nonclinical factors.

Authors

  • Kristin Putman
    From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 21201.
  • Mohamad El Moheb
    Department of Surgery, University of Virginia, Charlottesville, VA, USA.
  • Chengli Shen
    Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
  • Russell G Witt
    Department of Surgery, University of Virginia, Charlottesville, VA.
  • Samantha M Ruff
    Department of Surgery, University of Virginia, Charlottesville, VA, USA.
  • 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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