Сравнение на методи

	DeepSurv	Модел на ускорено време до отказ (AFT)	Регресия на пропорционалните опасности на Кокс
Област	Анализ на преживяемостта	Анализ на преживяемостта	Анализ на преживяемостта
Семейство	Survival analysis	Survival analysis	Survival analysis
Година на възникване	2018	1992	1972
Създател	Jared Katzman	Wei, L. J. (seminal review 1992); origins in parametric survival literature	Cox, D. R.
Тип	Neural network-based survival model	Parametric survival regression model	Semi-parametric hazard regression model
Основополагащ източник	Faraggi, D., & Simon, R. (1995). A neural network model for survival data. Statistics in Medicine, 14(1), 73–82.	Wei, L. J. (1992). The Accelerated Failure Time Model: A Useful Alternative to the Cox Regression Model in Survival Analysis. Statistics in Medicine, 11(14–15), 1871–1879.	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.
Други названия	Neural network survival, DL survival model	AFT model, parametric survival regression, Hızlandırılmış Başarısızlık Zamanı Modeli (AFT)	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu
Свързани	3	3	3
Резюме	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.	The Accelerated Failure Time model is a parametric regression approach to survival analysis — formally reviewed and advocated by L. J. Wei in 1992 — in which covariates act as multiplicative factors that directly stretch or compress the time-to-event scale. Unlike the Cox proportional-hazards model, which models how covariates shift the hazard rate, AFT models express the covariate effect as an acceleration or deceleration of the time axis itself.	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.