Development and validation of interpretable machine learning models for postoperative pneumonia prediction.

Journal: Frontiers in public health
Published Date: Dec 11, 2024

Abstract

BACKGROUND: Postoperative pneumonia, a prevalent form of hospital-acquired pneumonia, poses significant risks to patients' prognosis and even their lives. This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.

Authors

  • Bingbing Xiang
    Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China.
  • Yiran Liu
    Nursing Department, Chengfei Hospital, Chengdu, China.
  • Shulan Jiao
    Department of Anesthesiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Wensheng Zhang
    Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China.
  • Shun Wang
    Department of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
  • Mingliang Yi
    Department of Anesthesiology, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China.

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