DynSHAP：面向可解释动态生存分析
DynSHAP: Towards Explainable Dynamic Survival Analysis
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- University of Cambridge（剑桥大学）
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中文总结 AI 辅助
DynSHAP提出面向动态生存分析的SHAP解释框架，通过时间-特征对博弈和条件采样处理纵向不规则数据，在合成及真实临床数据上实现忠实归因。
中文摘要 AI 辅助
用于动态生存分析（DSA）的深度学习模型通过整合纵向患者数据实现了强大的预测性能，但其黑箱特性限制了临床信任和采用。现有的可解释性方法无法同时处理纵向、不规则输入和功能性生存输出，这限制了它们在DSA中的可用性。我们提出了DynSHAP，一个专门适用于动态生存分析的SHAP框架。它将常见的边际SHAP估计器扩展到该场景，通过将时间-特征对视为Shapley博弈中的玩家。我们进一步引入了Temporal DynSHAP，它学习特征随时间变化的线性依赖关系，并使用条件采样在解释中处理这些依赖。当应用于具有已知真实归因的合成数据时，对于给定的最先进模型，Temporal DynSHAP比边际估计器更准确地恢复时间依赖特征。应用于两个真实世界临床数据集和两种DSA架构时，DynSHAP产生的归因忠实于模型学习，使医学专家能够看到哪些患者信息驱动了预测以及何时驱动。
英文摘要
Deep 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. We propose DynSHAP, a SHAP framework suited specifically for dynamic survival analysis. It extends common marginal SHAP estimators to this setting by treating time--feature pairs as players in the Shapley game. We further introduce Temporal DynSHAP, which learns linear dependencies in features over time and uses conditional sampling to address them in explanations. When applied to synthetic data with known ground-truth attributions, Temporal DynSHAP recovers temporally dependent features more accurately than marginal estimators for a given state-of-the-art model. Applied to two real-world clinical datasets and two DSA architectures, DynSHAP produces attributions faithful to model learning, allowing medical experts to see which patient information drove the prediction and when.
