Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study.

Journal: PloS one
Published Date: Jan 1, 2021

Abstract

OBJECTIVE: To construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning.

Authors

  • Jae-Geum Shim
    Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Kyoung-Ho Ryu
    Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Sung Hyun Lee
    Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Eun-Ah Cho
    Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Sungho Lee
    Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Jin Hee Ahn
    Department of Cardiology and Medical Research Institute, Pusan National University Hospital, Busan, South Korea.

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