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arXiv Machine Learning By Mariana Vargas Vieyra, Pascal Frossard

Deep End-to-End Survival Analysis with Temporal Consistency

Read the original on arXiv Machine Learning →

The paper introduces a new survival analysis algorithm that leverages reinforcement learning concepts, specifically a delayed target network inspired by Deep Q-Networks, to enforce temporal consistency across consecutive survival predictions. This framework supports complex architectures and mini‑batch, end‑to‑end training, and demonstrates improved performance on long‑sequence datasets compared to benchmarks. Ablation studies reveal that the delay in target updates significantly affects predictive accuracy and training stability.

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