[2603.24828] A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproducibility Study

Computer Science > Machine Learning arXiv:2603.24828 (cs) [Submitted on 25 Mar 2026] Title:A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproducibility Study Authors:Yongda Fan, John Wu, Andrea Fitzpatrick, Naveen Baskaran, Jimeng Sun, Adam Cross View a PDF of the paper titled A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproducibility Study, by Yongda Fan and John Wu and Andrea Fitzpatrick and Naveen Baskaran and Jimeng Sun and Adam Cross View PDF HTML (experimental) Abstract:Clinical decisions are high-stakes and require explicit justification, making model interpretability essential for auditing deep clinical models prior to deployment. As the ecosystem of model architectures and explainability methods expands, critical questions remain: Do architectural features like attention improve explainability? Do interpretability approaches generalize across clinical tasks? While prior benchmarking efforts exist, they often lack extensibility and reproducibility, and critically, fail to systematically examine how interpretability varies across the interplay of clinical tasks and model architectures. To address these gaps, we present a comprehensive benchmark evaluating interpretability methods across diverse clinical prediction tasks and model architectures. Our analysis reveals that: (1) attention when leveraged properly is a highly efficient approach for faithfully interpreting model predictions...