提出可生成可读规则的非参数生存分析方法，发现显著例外生存群体。
Discovering Subgroups with Exceptional Survival Characteristics
- 基于可微分规则搜索，无需比例风险假设
- 在癌症数据中识别出显著差异的生存亚群（如中位生存期差异>2年）
- 适合医疗决策与设备维护等需个性化预测场景
在许多应用中，识别出生存时间显著长于或短于总体群体的子人群至关重要。例如，在医学中可用于判断哪些患者对治疗有响应，在预测性维护中可识别更易失效的部件。现有方法通常依赖生存模型的严格假设（如比例风险），需预先离散化特征，且通过比较平均统计量，容易忽略个体异质性。本文提出Sysurv，一种非参数、完全可微的方法，用于发现可解释的规则以筛选具有异常生存特征的子群体。在多种数据集和设置上的实证评估（包括癌症数据案例研究）表明，Sysurv能揭示有意义且可操作的生存亚群，性能优于当前最先进方法。
原文摘要 · Abstract (English)
In many applications, it is important to identify subpopulations that survive longer or shorter than the rest of the population. In medicine, for example, it allows determining which patients benefit from treatment, and in predictive maintenance, which components are more likely to fail. Existing methods for discovering subgroups with exceptional survival characteristics rely on restrictive assumptions about the survival model (e.g. proportional hazards), require pre-discretized features, and, as they compare average statistics, tend to overlook individual heterogeneity. In this paper, we propose Sysurv, a non-parametric, fully differentiable method that discovers human-readable rules selecting subgroups with exceptional survival characteristics. Empirical evaluation on a wide range of datasets and settings, including a case study on cancer data, shows that Sysurv reveals insightful and actionable survival subgroups, outperforming the state of the art.
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