Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.

Journal: Journal of translational medicine
Published Date: Dec 7, 2020

Abstract

BACKGROUND: Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models.

Authors

  • Nianzong Hou
    Department of Hand and Foot Surgery, Zibo Central Hospital, Shandong First Medical University, Zibo, 255036, Shandong, China.
  • Mingzhe Li
    Independent researcher, bs20m2l@leeds.ac.uk, Leeds, LS29JT, UK.
  • Lu He
    University of California, Irvine, Irvine, CA, USA.
  • Bing Xie
    Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Forensic Identification Center of Hebei Medical University, College of Forensic Medicine, Hebei Medical University, Shijiazhuang 050017, China.
  • Lin Wang
    Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
  • Rumin Zhang
    Department of Critical Care Medicine, Zibo Central Hospital, Shandong First Medical University , Zibo, 255036, Shandong, China.
  • Yong Yu
    Department of Automation, Xi'an Institute of High-Technology, Xi'an 710025, China, and Institute No. 25, Second Academy of China, Aerospace Science and Industry Corporation, Beijing 100854, China yuyongep@163.com.
  • Xiaodong Sun
    Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai JiaoTong University, 200080 Shanghai, China.
  • Zhengsheng Pan
    Department of Urology Surgery, Zibo Central Hospital, Shandong First Medical University , Zibo, 255036, China.
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.

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