An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia.

Journal: PloS one
PMID: 39774553

Abstract

BACKGROUND: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significantly associated with poor outcomes. However, there is currently a lack of reliable and interpretable tools for assessing the risk of in-hospital mortality in VAP patients. This study aims to develop an interpretable machine learning (ML) prediction model to enhance the assessment of in-hospital mortality risk in VAP patients.

Authors

  • Junying Wei
    Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
  • Heshan Cao
    Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
  • Mingling Peng
    First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
  • Yinzhou Zhang
    Department of Anesthesiology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Sibei Li
    Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Wuhua Ma
    Department of Anesthesiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
  • YuHui Li
    College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

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