Understanding risk factors for postoperative mortality in neonates based on explainable machine learning technology.

Journal: Journal of pediatric surgery
PMID:

Abstract

PURPOSE: We aimed to introduce an explainable machine learning technology to help clinicians understand the risk factors for neonatal postoperative mortality at different levels.

Authors

  • Yaoqin Hu
    The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
  • Xiaojue Gong
    The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
  • Liqi Shu
    Department of Neurology, The Warren Alpert Medical School of Brown University, Providence, RI, United States.
  • Xian Zeng
    The College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027 Hangzhou, Zhejiang, China.
  • Huilong Duan
    The College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027 Hangzhou, Zhejiang, China.
  • Qinyu Luo
    The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
  • Baihui Zhang
    The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
  • Yaru Ji
    The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
  • XiaoFeng Wang
    Indiana University Bloomington.
  • Qiang Shu
    Cardiac Surgery,Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.
  • Haomin Li
    Clinical Data Center, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, 310052 Hangzhou, Zhejiang, China.