A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care.

Journal: Critical care medicine
PMID: 32931194

Abstract

OBJECTIVES: As a life-threatening condition, sepsis is one of the major public health issues worldwide. Early prediction can improve sepsis outcomes with appropriate interventions. With the PhysioNet/Computing in Cardiology Challenge 2019, we aimed to develop and validate a machine learning algorithm with high prediction performance and clinical interpretability for prediction of sepsis onset during critical care in real-time.

Authors

  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Xiao Xu
    State Key Laboratory for Molecular Virology and Genetic Engineering, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
  • Fei Xie
    Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China.
  • Xian Xu
    Ping An Technology, Beijing, China.
  • Yuyao Sun
    Ping An Technology, Beijing, China.
  • Xiaoshuang Liu
    Ping An Technology, Beijing, China.
  • Xiaoyu Jia
    PingAn Health Technology, Beijing, China.
  • Yanni Kang
    PingAn Health Technology, Beijing, China.
  • Lixin Xie
    Department of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
  • Guotong Xie
    Ping An Health Technology, Beijing, China.

Keywords

External Resources

Popular Topics
Recent Journals