Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| DeepSurv× | Régression proportionnelle des risques de Cox× | |
|---|---|---|
| Domaine | Analyse de survie | Analyse de survie |
| Famille | Survival analysis | Survival analysis |
| Année d'origine≠ | 2018 | 1972 |
| Auteur d'origine≠ | Jared Katzman | Cox, D. R. |
| Type≠ | Neural network-based survival model | Semi-parametric hazard regression model |
| Source fondatrice≠ | 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 ↗ |
| Alias≠ | Neural network survival, DL survival model | cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu |
| Apparentées | 3 | 3 |
| Résumé≠ | 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. |
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