[Random survival forest: applying machine learning algorithm in survival analysis of biomedical data].

Journal: Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]
Published Date: Jan 6, 2021

Abstract

Traditional survival methods have a wide application in the field of biomedical research. However, applying traditional survival methods requires data to meet a set of special assumptions while the Random Survival Forest model can overcome this inconvenience. Herein, we used the clinical data of Primary Biliary Cholangitis (PBC) from Mayo Clinic to introduce and demonstrate Random Survival Forest model from mathematical principles, model building, practical example and attentions, aiming to provide a novel method for doing survival analysis.

Authors

  • Z Chen
    Department of Medical Microbiology, Capital Medical University, Beijing, China.
  • H M Xu
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Z X Li
    Department of Respiratory Diseases, Second Artillery General Hospital, Beijing 100088, People's Republic of China.
  • Y Zhang
    University Technology Sydney, 15 Broadway, Ultimo, NSW Australia.
  • T Zhou
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • W C You
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • K F Pan
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • W Q Li
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing 100142, China.

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