Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data.

Journal: BMC infectious diseases
PMID: 40269746

Abstract

OBJECTIVE: The aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients.

Authors

  • Jing Fu
    Shaoxing Second Hospital, 123 Yanan Road, Shaoxing, Zhejiang 312000, PR China.
  • Aifeng He
    Northern Jiangsu People's Hospital Affiliated to Yangzhou University/Clinical Medical College, Yangzhou University, Yangzhou, Jiangsu Province, China.
  • Lulu Wang
    c Center of Community Health Services, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang Province, China.
  • Xia Li
    Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
  • Jiangquan Yu
    Department of Critical Care Medicine, Northern Jiangsu People's Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China.
  • Ruiqiang Zheng
    Department of Critical Care Medicine, Northern Jiangsu People's Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China.

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