Research map / Probabilistic and scientific modeling / Molecules and proteins
Deep Extended Hazard Models for Survival Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| generalize DeepSurv (h1=0) and DeepAFT (h1=h2) to the full DeepEH with two separate network functions (ablation) | deep survival models · COLON, METABRIC, RotGBSG, WHAS | C-index | Helped |
| replace linear predictors of the EH model with neural networks (DeepEH vs EH) | extended hazard model · survival benchmarks | C-index | Helped |
| use DeepEH instead of Random Survival Forest | survival models · WHAS | C-index | Hurt |
| use DeepEH instead of the other ten survival methods | survival models · RotGBSG | IBS | Helped |
| DeepEH vs submodels (CoxPH, AFT, EH, DeepSurv, DeepAFT) (ablation) | survival models · all four datasets | IBS | Helped |
Closest papers by meaning
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- NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification (NeurIPS 2025 · similarity 0.86)
- Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees (AAAI 2024 · similarity 0.86)
- SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference (NeurIPS 2026 · similarity 0.85)
- A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature (NeurIPS 2026 · similarity 0.85)
- Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data (NeurIPS 2025 · similarity 0.85)
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