ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Failure Modes of Time Series Interpretability Algorithms for Critical Care Applications and Potential Solutions.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 time-series prediction problems provide more reliable and consistent interpretations for applications in critical care and similar domains.
Indexed as
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41726394PMC12919565What OpenQuestion holds
Registered trials
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