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| DeepSurv× | Regresja proporcjonalnego hazardu Coxa× | Parametryczny model regresji przeżycia Weibulla× | |
|---|---|---|---|
| Dziedzina | Analiza przeżycia | Analiza przeżycia | Analiza przeżycia |
| Rodzina | Survival analysis | Survival analysis | Survival analysis |
| Rok powstania≠ | 2018 | 1972 | 1951 |
| Twórca≠ | Jared Katzman | Cox, D. R. | Waloddi Weibull |
| Typ≠ | Neural network-based survival model | Semi-parametric hazard regression model | Fully parametric survival regression model |
| Źródło pierwotne≠ | Faraggi, D., & Simon, R. (1995). A neural network model for survival data. Statistics in Medicine, 14(1), 73–82. DOI ↗ | Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ | Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. DOI ↗ |
| Inne nazwy≠ | Neural network survival, DL survival model | cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma |
| Pokrewne≠ | 3 | 3 | 4 |
| Podsumowanie≠ | 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. | 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. |
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