FPBoost用可解释的参数化模型提升生存分析精度
FPBoost: Fully Parametric Gradient Boosting for Survival Analysis
- 用决策树优化参数化风险函数的加权组合，实现灵活建模
- 在多个数据集上表现优异，一致性与校准性均优于传统方法
- 适合医学、工程等需要高可解释性的生存预测场景
生存分析是建模事件发生时间的统计框架，在医学、可靠性工程和社会科学研究中至关重要。尽管机器学习中的神经网络和决策树已发展出复杂算法，但多数方法依赖于对事件时间分布的严格假设，如比例风险、时间离散化或加速失效时间。本文提出FPBoost，一种结合加权参数化风险函数与梯度提升的生存模型。通过最大化完整生存似然，利用决策树估计分布参数。我们证明了FPBoost能逼近任意风险函数，提供完整的事件时间建模灵活性，同时保持由成熟参数分布带来的可解释性。在多个基准数据集上评估了其一致性和校准性，验证了其作为新型生存估计工具的鲁棒性与通用性。
原文摘要 · Abstract (English)
Survival analysis is a statistical framework for modeling time-to-event data. It plays a pivotal role in medicine, reliability engineering, and social science research, where understanding event dynamics even with few data samples is critical. Recent advancements in machine learning, particularly those employing neural networks and decision trees, have introduced sophisticated algorithms for survival modeling. However, many of these methods rely on restrictive assumptions about the underlying event-time distribution, such as proportional hazard, time discretization, or accelerated failure time. In this study, we propose FPBoost, a survival model that combines a weighted sum of fully parametric hazard functions with gradient boosting. Distribution parameters are estimated with decision trees trained by maximizing the full survival likelihood. We show how FPBoost is a universal approximator of hazard functions, offering full event-time modeling flexibility while maintaining interpretability through the use of well-established parametric distributions. We evaluate concordance and calibration of FPBoost across multiple benchmark datasets, showcasing its robustness and versatility as a new tool for survival estimation.
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