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 shortcom-ings of the Cox proportional hazard model in terms of nonlinear data processing and prediction accuracy.This article re-viewed 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 sur-vival forest,providing methodological insights for the analysis of complex survival data.
万方数据
维普
