用时序一致性提升生存分析模型在长序列数据上的稳定性与效果
Deep End-to-End Survival Analysis with Temporal Consistency
- 借鉴强化学习思想，引入时序一致性约束提升训练稳定性
- 在长序列数据上优于基准模型，显著捕捉长期时间依赖关系
- 支持复杂架构端到端训练，适合大规模纵向数据分析
本文提出一种新型生存分析算法，用于高效处理大规模纵向数据。方法受强化学习中深度Q网络启发，将时序学习概念扩展至生存回归任务。核心思想是时序一致性——假设数据中的过去与未来结果随时间平滑演化。通过提供稳定的训练信号，该框架在大规模数据中有效建模长期时序关系并确保可靠更新。方法支持任意复杂架构，可建模复杂时序依赖，并实现端到端训练。大量实验验证了其在不同规模数据集上对时序一致性的有效利用。尤其在长序列数据上表现优于基准方法，展现出捕捉长期模式的能力。消融实验进一步表明该方法提升了训练稳定性。
原文摘要 · Abstract (English)
In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspiration from Reinforcement Learning principles, particularly the Deep Q-Network paradigm, extending Temporal Learning concepts to Survival Regression. A central idea in our method is temporal consistency, a hypothesis that past and future outcomes in the data evolve smoothly over time. Our framework uniquely incorporates temporal consistency into large datasets by providing a stable training signal that captures long-term temporal relationships and ensures reliable updates. Additionally, the method supports arbitrarily complex architectures, enabling the modeling of intricate temporal dependencies, and allows for end-to-end training. Through numerous experiments we provide empirical evidence demonstrating our framework's ability to exploit temporal consistency across datasets of varying sizes. Moreover, our algorithm outperforms benchmarks on datasets with long sequences, demonstrating its ability to capture long-term patterns. Finally, ablation studies show how our method enhances training stability.
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