基于时间一致性的深度端到端生存分析

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Abstract: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 argument in our method relies on temporal consistency, a recursive consistency condition between survival predictions made at consecutive states. Our framework introduces a delayed target network that decouples the generation of temporally consistent training targets from model updates, providing a more stable training signal while enabling mini-batch optimization and end-to-end training. Additionally, it supports arbitrarily complex architectures, enabling the modeling of intricate temporal dependencies. 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 that the degree of target delay has a substantial effect on predictive performance and training variability. Across two distinct survival model families, delayed target updates consistently improve over the same-timescale baseline.

Subjects:	Machine Learning (cs.LG)
Cite as:	arXiv:2410.06786 [cs.LG]
	(or arXiv:2410.06786v2 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2410.06786 arXiv-issued DOI via DataCite

Submission history

From: Mariana Vargas Vieyra [view email]
[v1] Wed, 9 Oct 2024 11:37:09 UTC (284 KB)
[v2] Fri, 9 Oct 2026 14:30:22 UTC (285 KB)