Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study.

Journal: Journal of medical Internet research
PMID:

Abstract

BACKGROUND: Persistent sepsis-associated acute kidney injury (SA-AKI) shows poor clinical outcomes and remains a therapeutic challenge for clinicians. Early identification and prediction of persistent SA-AKI are crucial.

Authors

  • Wei Jiang
    Department of Civil Engineering, Johns Hopkins System Institute, Johns Hopkins University, Baltimore, Maryland.
  • Yaosheng Zhang
    School of Clinical and Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.
  • Jiayi Weng
    School of Economics and Management, Beijing Jiao Tong University, Beijing, China.
  • Lin Song
    Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, 160 Pujian Road, Pudong District, Shanghai, 200127, China.
  • Siqi Liu
    National University of Singapore Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore.
  • Xianghui Li
    School of Pharmacy, Second Military Medical University, Shanghai 200433, China.
  • Shiqi Xu
    Department of Electrical & System Engineering, Washington University in St. Louis.
  • Keran Shi
    Department of Critical Care Medicine, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
  • Luanluan Li
    Department of Critical Care Medicine, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
  • Chuanqing Zhang
    Department of Critical Care Medicine, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Quan Yuan
    School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
  • Yongwei Zhang
    School of Automation, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: Yongwei_Zhang@mail2.gdut.edu.cn.
  • Jun Shao
    Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • 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.