Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| DeepSurv× | Regresión de Supervivencia Paramétrica de Weibull× | |
|---|---|---|
| Campo | Supervivencia | Supervivencia |
| Familia | Survival analysis | Survival analysis |
| Año de origen≠ | 2018 | 1951 |
| Autor original≠ | Jared Katzman | Waloddi Weibull |
| Tipo≠ | Neural network-based survival model | Fully parametric survival regression model |
| Fuente seminal≠ | 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 ↗ |
| Alias≠ | Neural network survival, DL survival model | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma |
| Relacionados≠ | 3 | 4 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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