Application of machine learning method for survival analysis
10.19485/j.cnki.issn2096-5087.2024.06.009
- Author:
LIU Yue
;
LIU Qiling
;
SU Haixia
;
YANG Peng
;
ZHANG Yuhai
- Publication Type:Journal Article
- Keywords:
survival analysis;
machine learning;
DeepSurv;
Deep-Hit;
random survival forest
- From:
Journal of Preventive Medicine
2024;36(6):496-500,505
- CountryChina
- Language:Chinese
-
Abstract:
Abstract:Survival analysis has been widely used in the field of medical research. The Cox proportional hazard model is commonly used, but its practical application is limited. Machine learning method can compensate for the shortcomings of the Cox proportional hazard model in terms of nonlinear data processing and prediction accuracy. This article reviewed the advance of machine learning methods represented by neural networks, within the field of survival analysis, and highlighted the principles and benefits of three machine learning methods that DeepSurv, Deep-Hit and random survival forest, providing methodological insights for the analysis of complex survival data.
- Full text:2024072509231511514机器学习法在生存分析中的应用研究.pdf
