首个融合贝叶斯不确定性的深度生存分析模型，提升小样本下的预测可靠性。
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
- 通过两阶段数据增强捕捉连续时间中的动态风险关系
- 相比现有模型校准性更优，判别性能相当或更佳
- 适合医疗预测等需可信不确定度估计的场景
我们提出 NeuralSurv，首个融合贝叶斯不确定性量化机制的深度生存分析模型。该非参数化、架构无关的框架通过创新的两阶段数据增强方案，在连续时间中捕捉时变协变量-风险关系，并给出了理论保证。为实现高效后验推断，我们设计了一种均值场变分算法，采用坐标上升更新，计算复杂度随模型规模线性增长。通过局部线性化贝叶斯神经网络，获得完全共轭性，所有坐标更新均可解析求解。实验表明，NeuralSurv 在合成基准与真实数据集上均优于现有深度生存模型的校准性，判别性能相当或更优。结果证明，在数据稀缺场景下，贝叶斯方法能显著提升模型校准性，并提供稳健的生存函数不确定性估计。
原文摘要 · Abstract (English)
We introduce NeuralSurv, the first deep survival model to incorporate Bayesian uncertainty quantification. Our non-parametric, architecture-agnostic framework captures time-varying covariate-risk relationships in continuous time via a novel two-stage data-augmentation scheme, for which we establish theoretical guarantees. For efficient posterior inference, we introduce a mean-field variational algorithm with coordinate-ascent updates that scale linearly in model size. By locally linearizing the Bayesian neural network, we obtain full conjugacy and derive all coordinate updates in closed form. In experiments, NeuralSurv delivers superior calibration compared to state-of-the-art deep survival models, while matching or exceeding their discriminative performance across both synthetic benchmarks and real-world datasets. Our results demonstrate the value of Bayesian principles in data-scarce regimes by enhancing model calibration and providing robust, well-calibrated uncertainty estimates for the survival function.
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