Advanced Non-linear Modeling and Explainable Artificial Intelligence Techniques for Predicting 30-Day Complications in Bariatric Surgery: A Single-Center Study.

Journal: Obesity surgery
PMID: 39271585

Abstract

PURPOSE: Metabolic bariatric surgery (MBS) became integral to managing severe obesity. Understanding surgical risks associated with MBS is crucial. Different scores, such as the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP), aid in patient selection and outcome prediction. This study aims to evaluate machine learning (ML) models performance in predicting 30-day post-operative complications and compare them with the MBSAQIP risk score.

Authors

  • Nicolas Zucchini
    Department of Medical, Surgical and Health Sciences, University of Trieste, Strada Di Fiume, 447, 34149, Trieste, Italy.
  • Eugenia Capozzella
    Department of Medical, Surgical and Health Sciences, University of Trieste, Strada Di Fiume, 447, 34149, Trieste, Italy.
  • Mauro Giuffrè
    Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, USA.
  • Manuela Mastronardi
    Department of Medical, Surgical and Health Sciences, University of Trieste, Strada Di Fiume, 447, 34149, Trieste, Italy. manuela.mastronardi@gmail.com.
  • Biagio Casagranda
    Surgical Clinic Division, Cattinara Hospital, ASUGI, 34149, Trieste, Italy.
  • Saveria Lory Crocè
    Department of Medical, Surgical and Health Sciences, University of Trieste, Strada Di Fiume, 447, 34149, Trieste, Italy.
  • Nicolò de Manzini
    Department of Medical, Surgical and Health Sciences, University of Trieste, Strada Di Fiume, 447, 34149, Trieste, Italy.
  • Silvia Palmisano
    Department of Medical, Surgical and Health Sciences, University of Trieste, Strada Di Fiume, 447, 34149, Trieste, Italy.

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