Interpretable Deep Survival Learning for Dynamic Risk Prediction in Healthcare Decision Support

Abstract

Dynamic risk prediction in healthcare relies on complex temporal data to anticipate patient outcomes, yet the deployment of deep survival models in clinical decision support systems remains constrained by concerns over interpretability, fairness, and operational sustainability. This paper presents a comprehensive systems-level analysis of interpretable deep survival learning architectures, emphasizing the structural trade-offs between predictive accuracy and model transparency. We examine the integration of attention mechanisms, time-dependent feature attribution, and counterfactual explanations into survival frameworks that operate on electronic health records and clinical trial data. The discussion extends to deployment infrastructure, including real-time inference pipelines, data governance, and computational efficiency across distributed healthcare networks. Particular attention is given to fairness implications, where uncalibrated survival models may amplify disparities in risk assessment across demographic groups, and to policy requirements for regulatory validation under frameworks such as those advanced by the Food and Drug Administration. Through cross-domain comparisons with interpretable methods in genomics and imaging, we identify common architectural patterns that support robust, auditable risk prediction. The paper concludes with forward-looking perspectives on federated learning, continual model updating, and the governance of algorithmic accountability in high-stakes medical environments. By situating interpretable deep survival learning within broader socio-technical infrastructures, we argue that sustainable deployment demands not only methodological innovation but also institutional frameworks for monitoring, updating, and contesting model-driven decisions.