Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters.

Journal: BMC anesthesiology
PMID: 39695971

Abstract

BACKGROUND: To protect patients during anesthesia, difficult airway management is a serious issue that needs to be carefully planned for and carried out. Machine learning prediction tools have recently become increasingly common in medicine, frequently surpassing more established techniques. This study aims to utilize machine learning techniques on predictive parameters for challenging airway management.

Authors

  • Parisa Sezari
    Department of Anesthesiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Zeinab Kohzadi
    Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, 1th floor, No 21, Darband St., Tajrish sq., Tehran, Iran. z.kohzadi96@gmail.com.
  • Ali Dabbagh
    Wellness Research Cluster, Institute of Research Management and Monitoring (IPPP), University of Malaya, Kuala Lumpur, Malaysia.
  • Alireza Jafari
    Inflammatory Lung Disease Research Center, Department of Internal Medicine, Razi Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran; Urology Research Center, Razi Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran. Electronic address: Dr.alireza.jafariii@gmail.com.
  • Saba Khoshtinatan
    Department of Anesthesiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Kamran Mottaghi
    Department of Anesthesiology, School of Medicine, Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Zahra Kohzadi
    Ilam County Health Center, Ilam University of Medical Sciences, Ilam, Iran.
  • Shahabedin Rahmatizadeh
    Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, 1th floor, No 21, Darband St., Tajrish sq., Tehran, Iran.

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