An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.

Journal: Critical care medicine
Published Date: Nov 1, 2020

Abstract

OBJECTIVES: Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial intelligence model for early predicting sepsis by analyzing the electronic health record data from ICU provided by the PhysioNet/Computing in Cardiology Challenge 2019.

Authors

  • Meicheng Yang
    The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
  • Chengyu Liu
    Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Xingyao Wang
    The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
  • Yuwen Li
  • Hongxiang Gao
    The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
  • Xing Liu
    School of Food Science and Engineering, Hainan University 58 Renmin Avenue Haikou 570228 China zhangzeling@hainanu.edu.cn benchao312@hainanu.edu.cn xuhuan.hnu@foxmail.com qichen@hainanu.edu.cn sunzhichang11@163.com hmcao@hainanu.edu.cn.
  • Jianqing Li
    School of Instrument Science and Engineering, Southeast University, Sipailou 2, Nanjing 210096, China.

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