Random survival forest: applying machine learning algorithm in survival analysis of biomedical data
10.3760/cma.j.cn112150-20200911-01197
- VernacularTitle:随机生存森林：基于机器学习算法的生存分析模型
- Author:
Zhe CHEN
1
;
Hengmin XU
;
Zhexuan LI
;
Yang ZHANG
;
Tong ZHOU
;
Weicheng YOU
;
Kaifeng PAN
;
Wenqing LI
Author Information
1. 北京大学肿瘤医院暨北京市肿瘤防治研究所流行病学研究室 恶性肿瘤发病机制及转化研究教育部重点实验室 100142
- Keywords:
Models, statistical;
Artificial intelligence;
Random survival forest
- From:
Chinese Journal of Preventive Medicine
2021;55(1):104-109
- CountryChina
- Language:Chinese
-
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.
