Data-driven Machine Learning Models for Risk Stratification and Prediction of Emergence Delirium in Pediatric Patients Underwent Tonsillectomy/Adenotonsillectomy.

Journal: Annali italiani di chirurgia
Published Date: Jan 1, 2024

Abstract

AIM: In the pediatric surgical population, Emergence Delirium (ED) poses a significant challenge. This study aims to develop and validate machine learning (ML) models to identify key features associated with ED and predict its occurrence in children undergoing tonsillectomy or adenotonsillectomy.

Authors

  • Alessandro Simonini
    Pediatric Anesthesia and Intensive Care Unit AOU delle Marche, Salesi Children's Hospital, 60121, Ancona, Italy.
  • Jeevitha Murugan
    BTech - Artificial Intelligence and Data Science, St Joseph's College of Engineering, 600119 Chennai, India.
  • Alessandro Vittori
    Department of Anesthesia and Critical Care, ARCO ROMA, Ospedale Pediatrico Bambino Gesù IRCCS, Piazza S. Onofrio 4, 00165, Rome, Italy.
  • Roberta Pallotto
    Department of Pediatric Anaesthesia and Intensive Care, S.C. SOD Anestesia e Rianimazione Pediatrica, Ospedale G. Salesi, 60123 Ancona, Italy.
  • Elena Giovanna Bignami
    Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy.
  • Maria Grazia Calevo
    Epidemiology and Biostatistic Unit, Scientific Directorate, IRCCS Istituto Giannini Gaslini, 16147 Genoa, Italy.
  • Ornella Piazza
    Anesthesia and Pain Medicine, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Baronissi, 84081, Italy.
  • Marco Cascella
    Department of Medicine, Surgery and Dentistry, University of Salerno, 84081, Baronissi, Italy.

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