Сравнение методов

	DeepSurv	Вейбулловская параметрическая регрессия выживаемости
Область	Анализ выживаемости	Анализ выживаемости
Семейство	Survival analysis	Survival analysis
Год появления	2018	1951
Автор метода	Jared Katzman	Waloddi Weibull
Тип	Neural network-based survival model	Fully parametric survival regression model
Основополагающий источник	Faraggi, D., & Simon, R. (1995). A neural network model for survival data. Statistics in Medicine, 14(1), 73–82.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
Другие названия	Neural network survival, DL survival model	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Связанные	3	4
Сводка	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.	Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival.