Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study.

Journal: Frontiers in cellular and infection microbiology
PMID: 39844844

Abstract

BACKGROUND: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pose a significant global health challenge, with sepsis-related deaths contributing substantially to the overall burden on healthcare systems worldwide. The primary objective was to construct and evaluate a machine learning (ML) model for forecasting 28-day all-cause mortality among ICU sepsis patients.

Authors

  • Li Shen
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Jiaqiang Wu
    School of Life Sciences and Biopharmaceutical Science, Shenyang Pharmaceutical University, Shenyang, China.
  • Jianger Lan
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Chao Chen
    Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Yi Wang
    Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Zhiping Li
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.

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