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| DeepSurv× | Accelerated Failure Time (AFT) Modell× | |
|---|---|---|
| Fachgebiet | Überlebenszeitanalyse | Überlebenszeitanalyse |
| Familie | Survival analysis | Survival analysis |
| Entstehungsjahr≠ | 2018 | 1992 |
| Urheber≠ | Jared Katzman | Wei, L. J. (seminal review 1992); origins in parametric survival literature |
| Typ≠ | Neural network-based survival model | Parametric survival regression model |
| Wegweisende Quelle≠ | Faraggi, D., & Simon, R. (1995). A neural network model for survival data. Statistics in Medicine, 14(1), 73–82. DOI ↗ | Wei, L. J. (1992). The Accelerated Failure Time Model: A Useful Alternative to the Cox Regression Model in Survival Analysis. Statistics in Medicine, 11(14–15), 1871–1879. DOI ↗ |
| Aliasnamen≠ | Neural network survival, DL survival model | AFT model, parametric survival regression, Hızlandırılmış Başarısızlık Zamanı Modeli (AFT) |
| Verwandt | 3 | 3 |
| Zusammenfassung≠ | 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. | The Accelerated Failure Time model is a parametric regression approach to survival analysis — formally reviewed and advocated by L. J. Wei in 1992 — in which covariates act as multiplicative factors that directly stretch or compress the time-to-event scale. Unlike the Cox proportional-hazards model, which models how covariates shift the hazard rate, AFT models express the covariate effect as an acceleration or deceleration of the time axis itself. |
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