DynSHAP: Towards Explainable Dynamic Survival Analysis
DynSHAP extends SHAP explainability to dynamic survival analysis, treating time-feature pairs as Shapley players for longitudinal clinical predictions.
DynSHAP adapts marginal SHAP estimators to dynamic survival analysis by treating time-feature pairs as players in the Shapley game, handling longitudinal irregular inputs and functional survival outputs. Temporal DynSHAP learns linear feature dependencies over time and addresses them with conditional sampling. On synthetic data with ground-truth attributions it recovers temporally dependent features more accurately than marginal estimators, and it produces faithful attributions on two real-world clinical datasets across two DSA architectures.
- Extends SHAP to longitudinal, irregular inputs and functional survival outputs
- Temporal DynSHAP handles feature dependencies over time via conditional sampling
- Recovers ground-truth attributions better than marginal estimators on synthetic data
- Validated on two real-world clinical datasets and two DSA architectures
From the original article
1 min read at arxiv.orgDeep learning models for dynamic survival analysis (DSA) achieve strong predictive performance by incorporating longitudinal patient data, but their black box nature limits clinical trust and adoption. Existing explainability methods cannot handle longitudinal, irregular inputs and functional survival outputs simultaneously, which limits their usability in DSA.
By Nastasya Anokhina · arxiv.org
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