Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study.

Journal: Journal of medical Internet research
Published Date: May 26, 2025

Abstract

BACKGROUND: Sepsis-associated liver injury (SALI) is a severe complication of sepsis that contributes to increased mortality and morbidity. Early identification of SALI can improve patient outcomes; however, sepsis heterogeneity makes timely diagnosis challenging. Traditional diagnostic tools are often limited, and machine learning techniques offer promising solutions for predicting adverse outcomes in patients with sepsis.

Authors

  • Jingchao Lei
    Department of Emergency, The Third Xiangya Hospital of Central South University, Changsha, 410013, China.
  • Jia Zhai
    Department of Emergency, The Third Xiangya Hospital of Central South University, Changsha, 410013, China.
  • Yao Zhang
    Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Jing Qi
    China Meat Research Center, Beijing 100068, China.
  • Chuanzheng Sun
    Department of Emergency, The Third Xiangya Hospital of Central South University, Changsha, 410013, China. sunchuanzheng@csu.edu.cn.

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