Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| DeepHit× | DeepSurv× | |
|---|---|---|
| Галузь | Аналіз виживаності | Аналіз виживаності |
| Родина | Survival analysis | Survival analysis |
| Рік появи | 2018 | 2018 |
| Автор методу≠ | Changhee Lee | Jared Katzman |
| Тип≠ | Neural network competing risks model | Neural network-based survival model |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | Neural network competing risks, DL competing events | Neural network survival, DL survival model |
| Пов'язані≠ | 1 | 3 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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