Failure Modes of Time Series Interpretability Algorithms for Critical Care Applications and Potential Solutions

• URL: http://arxiv.org/abs/2506.19035v1
• Date: Mon, 23 Jun 2025 18:45:47 GMT
• Title: Failure Modes of Time Series Interpretability Algorithms for Critical Care Applications and Potential Solutions
• Authors: Shashank Yadav, Vignesh Subbian,
• Abstract summary: Interpretability plays a vital role in aligning and deploying deep learning models in critical care.<n> Gradient, Occlusion, and Permutation-based methods often struggle with time-varying target dependency and temporal smoothness.<n>This work systematically analyzes these failure modes and supports learnable mask-based interpretability frameworks.
• Score: 1.277162574025857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 timeseries prediction problems provide more reliable and consistent interpretations for applications in critical care and similar domains.

Related papers

• Robust Multi-Modal Forecasting: Integrating Static and Dynamic Features [0.0]
  Time series forecasting plays a crucial role in various applications, particularly in healthcare.<n> Ensuring transparency and explainability of the models responsible for these tasks is essential for their adoption in critical settings.<n>Recent work has explored a top-down approach to bi-level transparency, focusing on understanding trends and properties of predicted time series.
  arXiv  Detail & Related papers  (2025-05-21T04:12:12Z)
• SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation [33.02002580363215]
  Vision-language temporal alignment is a crucial capability for human dynamic recognition and cognition in real-world scenarios.<n>We introduce SVLTA, the Synthetic Vision-Language Temporal Alignment derived via a well-designed and feasible control generation method within a simulation environment.<n>Our experiments reveal diagnostic insights through the evaluations in temporal question answering, distributional shift sensitiveness, and temporal alignment adaptation.
  arXiv  Detail & Related papers  (2025-04-08T11:31:37Z)
• An Adversarial Learning Approach to Irregular Time-Series Forecasting [0.032771631221674334]
  We propose an adversarial learning framework with a deep analysis of adversarial components to better capture the nuances of irregular time series.<n>Overall, this research provides practical insights for improving models and evaluation metrics, and pioneers the application of adversarial learning in the domian of irregular time-series forecasting.
  arXiv  Detail & Related papers  (2024-11-28T19:28:07Z)
• On the Regularization of Learnable Embeddings for Time Series Forecasting [18.069747511100132]
  We investigate methods to regularize the learning of local learnable embeddings for time series processing.<n>We show that methods attempting to prevent the co-adaptation of local and global parameters by means of embeddings perturbation are particularly effective in this context.
  arXiv  Detail & Related papers  (2024-10-18T17:30:20Z)
• EMIT- Event-Based Masked Auto Encoding for Irregular Time Series [9.903108445512576]
  Irregular time series, where data points are recorded at uneven intervals, are prevalent in healthcare settings. This variability, which reflects critical fluctuations in patient health, is essential for informed clinical decision-making. Existing self-supervised learning research on irregular time series often relies on generic pretext tasks like forecasting. This paper proposes a novel pretraining framework, EMIT, an event-based masking for irregular time series.
  arXiv  Detail & Related papers  (2024-09-25T02:05:32Z)
• Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series [10.388704631887496]
  We introduce Sequence Dreaming, a technique that adapts Maxim Activationization to analyze sequential information. We visualize the temporal dynamics and patterns most influential in model decision-making processes.
  arXiv  Detail & Related papers  (2024-08-20T08:09:44Z)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics [51.147876395589925]
  A non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time. A fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance.
  arXiv  Detail & Related papers  (2024-02-26T04:39:01Z)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• Stateful Offline Contextual Policy Evaluation and Learning [88.9134799076718]
  We study off-policy evaluation and learning from sequential data. We formalize the relevant causal structure of problems such as dynamic personalized pricing. We show improved out-of-sample policy performance in this class of relevant problems.
  arXiv  Detail & Related papers  (2021-10-19T16:15:56Z)
• Interpretable Time-series Representation Learning With Multi-Level Disentanglement [56.38489708031278]
  Disentangle Time Series (DTS) is a novel disentanglement enhancement framework for sequential data. DTS generates hierarchical semantic concepts as the interpretable and disentangled representation of time-series. DTS achieves superior performance in downstream applications, with high interpretability of semantic concepts.
  arXiv  Detail & Related papers  (2021-05-17T22:02:24Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.