Research map / Probabilistic and scientific modeling / Molecules and proteins
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add ranking loss L2 (α>0) (ablation) | DeepHit · SEER Breast Cancer | Ctd-index: 0.736 -> 0.752 | Helped |
| add ranking loss L2 (α>0) (ablation) | DeepHit · SEER CVD | Ctd-index: 0.674 -> 0.684 | Helped |
| add ranking loss L2 (α>0) (ablation) | DeepHit · SYNTHETIC Event 1 | Ctd-index: 0.739 -> 0.755 | Helped |
| add ranking loss L2 (α>0) (ablation) | DeepHit · UNOS | Ctd-index: 0.573 -> 0.589 | Helped |
| add ranking loss L2 (α>0) (ablation) | DeepHit · METABRIC | Ctd-index: 0.646 -> 0.691 | Helped |
| nonparametric deep network instead of cs-Cox | survival model · SEER CVD | Ctd-index: 0.672 -> 0.684 | No clear effect |
| nonparametric deep network instead of DeepSurv | survival model · METABRIC | Ctd-index: 0.648 -> 0.691 | Helped |
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- SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference (NeurIPS 2026 · similarity 0.84)
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