Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| DeepHit× | DeepSurv× | |
|---|---|---|
| Nozare | Dzīvildze | Dzīvildze |
| Saime | Survival analysis | Survival analysis |
| Izcelsmes gads | 2018 | 2018 |
| Autors≠ | Changhee Lee | Jared Katzman |
| Tips≠ | Neural network competing risks model | Neural network-based survival model |
| Pirmavots≠ | Lee, C., Zame, W., Yoon, J., & van der Schaar, M. (2018). DeepHit: A deep learning approach for dynamic survival analysis with competing risks. AAAI Conference on Artificial Intelligence, 32(1), 2314–2321. link ↗ | Faraggi, D., & Simon, R. (1995). A neural network model for survival data. Statistics in Medicine, 14(1), 73–82. DOI ↗ |
| Citi nosaukumi | Neural network competing risks, DL competing events | Neural network survival, DL survival model |
| Saistītās≠ | 1 | 3 |
| Kopsavilkums≠ | DeepHit is a deep neural network framework for survival analysis with competing risks. Introduced by Lee et al. in 2018, it extends DeepSurv to handle settings where multiple, mutually exclusive events can occur, such as disease-specific mortality versus death from other causes. DeepHit solves the challenge of personalized risk prediction when subjects can experience different types of terminal events, a common scenario in medical and reliability applications. | 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. |
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