针对重症监护时序模型解释的失效问题，提出可学习掩码框架提升解释可靠性。
Failure Modes of Time Series Interpretability Algorithms for Critical Care Applications and Potential Solutions
- 采用可学习掩码机制建模时间连续性与标签一致性
- 揭示梯度/遮蔽/置换法在动态预测中的解释失效问题
- 适合需高可靠解释的医疗时序分析场景
在重症监护等动态预测任务中，深度学习模型的可解释性至关重要，但常见解释算法（如梯度、遮蔽、置换）面临目标依赖随时间变化及时间平滑性不足的问题。本文系统分析了这些方法的失效模式，提出以可学习掩码为基础的解释框架，该框架能引入时间连续性和标签一致性约束，从而在动态时序预测中更稳定地学习特征重要性。实验表明，此类方法在重症监护场景下能提供更可靠、一致的解释，适用于对解释质量要求高的临床决策支持系统。
原文摘要 · Abstract (English)
Interpretability plays a vital role in aligning and deploying deep learning models in critical care, especially in constantly evolving conditions that influence patient survival. However, common interpretability algorithms face unique challenges when applied to dynamic prediction tasks, where patient trajectories evolve over time. Gradient, Occlusion, and Permutation-based methods often struggle with time-varying target dependency and temporal smoothness. This work systematically analyzes these failure modes and supports learnable mask-based interpretability frameworks as alternatives, which can incorporate temporal continuity and label consistency constraints to learn feature importance over time. Here, we propose that learnable mask-based approaches for dynamic timeseries prediction problems provide more reliable and consistent interpretations for applications in critical care and similar domains.
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