방법 비교

	DeepSurv	Cox 비례 위험 회귀분석
분야	생존분석	생존분석
계열	Survival analysis	Survival analysis
기원 연도	2018	1972
창시자	Jared Katzman	Cox, D. R.
유형	Neural network-based survival model	Semi-parametric hazard regression model
원전	Faraggi, D., & Simon, R. (1995). A neural network model for survival data. Statistics in Medicine, 14(1), 73–82.	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.
별칭	Neural network survival, DL survival model	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu
관련	3	3
요약	DeepSurv is a deep neural network approach to survival analysis that learns personalized survival distributions directly from data. Introduced by Katzman et al. in 2018, it extends the Cox proportional hazards model using deep learning to capture complex, nonlinear relationships between covariates and survival outcomes. It solves the problem of modeling heterogeneous treatment effects and time-to-event predictions in high-dimensional settings.	Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.