Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).

Journal: BMC emergency medicine
Published Date: Feb 11, 2022

Abstract

BACKGROUND: Elderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We planned to use the laboratory test results and comorbidities of elderly patients with sepsis from a large-scale public database Medical Information Mart for Intensive Care (MIMIC) IV to build a random survival forest (RSF) model and to evaluate the model's predictive value for these patients.

Authors

  • Luming Zhang
  • Tao Huang
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Fengshuo Xu
    Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.
  • Shaojin Li
    Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.
  • Shuai Zheng
    Anhui Agricultural University Hefei 230036 PR China.
  • Jun Lyu
    Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
  • Haiyan Yin
    Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, People's Republic of China. yinhaiyan1867@126.com.

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