Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps.

Journal: Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
PMID: 39109612

Abstract

OBJECTIVE: Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence.

Authors

  • Anda Gata
    Department of Otorhinolaryngology, University of Medicine and Pharmacy "Iuliu Hatieganu", Cluj Napoca, Romania.
  • Lajos Raduly
    Research Center for Functional Genomics, Biomedicine and Translational Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania.
  • Liviuța Budișan
    Research Center for Functional Genomics, Biomedicine and Translational Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania.
  • Adél Bajcsi
    Faculty of Mathematics and Computer Science, Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania.
  • Teodora-Maria Ursu
    Faculty of Mathematics and Computer Science, Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania.
  • Camelia Chira
    Faculty of Mathematics and Computer Science, Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania.
  • Laura Dioşan
    Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania.
  • Ioana Berindan-Neagoe
    Research Center for Functional Genomics, Biomedicine and Translational Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania.
  • Silviu Albu
    Department of Otorhinolaryngology, University of Medicine and Pharmacy "Iuliu Hatieganu", Cluj Napoca, Romania.

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