Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis.

Journal: BMC infectious diseases
PMID: 39709376

Abstract

BACKGROUND: Predicting mortality in sepsis-related acute kidney injury facilitates early data-driven treatment decisions. Machine learning is predicting mortality in S-AKI in a growing number of studies. Therefore, we conducted this systematic review and meta-analysis to investigate the predictive value of machine learning for mortality in patients with septic acute kidney injury.

Authors

  • Xiangui Lv
    Department of Intensive Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, Sichuan, China.
  • Daiqiang Liu
    Department of Intensive Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, Sichuan, China.
  • Xinwei Chen
    National Soybean Processing Industry Technology Innovation Center, Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University Beijing 100048 China lihe@btbu.edu.cn liuxinqi@btbu.edu.cn.
  • Lvlin Chen
    Department of Intensive Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, Sichuan, China.
  • Xiaohui Wang
    School of Kinesiology, Shanghai University of Sport, Shanghai 200438, China.
  • Xiaomei Xu
    College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, 210037, China.
  • Lin Chen
    College of Sports, Nanjing Tech University, Nanjing, China.
  • Chao Huang
    University of North Carolina, Chapel Hill, NC, USA.

Keywords

External Resources

Popular Topics
Recent Journals