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| DeepSurv× | Регресия на Вайбул за оцеляване (Weibull Parametric Survival Regression)× | |
|---|---|---|
| Област | Анализ на преживяемостта | Анализ на преживяемостта |
| Семейство | Survival analysis | Survival analysis |
| Година на възникване≠ | 2018 | 1951 |
| Създател≠ | Jared Katzman | Waloddi Weibull |
| Тип≠ | Neural network-based survival model | Fully parametric survival regression model |
| Основополагащ източник≠ | Faraggi, D., & Simon, R. (1995). A neural network model for survival data. Statistics in Medicine, 14(1), 73–82. DOI ↗ | Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. DOI ↗ |
| Други названия≠ | Neural network survival, DL survival model | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma |
| Свързани≠ | 3 | 4 |
| 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. | 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. |
| ScholarGateНабор от данни ↗ |
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