Learning and Naming Subgroups with Exceptional Survival Characteristics

• URL: http://arxiv.org/abs/2602.22179v1
• Date: Wed, 25 Feb 2026 18:25:47 GMT
• Title: Learning and Naming Subgroups with Exceptional Survival Characteristics
• Authors: Mhd Jawad Al Rahwanji, Sascha Xu, Nils Philipp Walter, Jilles Vreeken,
• Abstract summary: In medicine, it is important to identify subpopulations that survive longer or shorter than the rest of the population.<n>We propose Sysurv, a non-parametric method that learns individual survival curves, automatically learns conditions and how to combine these into inherently interpretable rules.<n> 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.
• Score: 32.19880761967807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 require restrictive assumptions about the survival model (e.g. proportional hazards), pre-discretized features, and, as they compare average statistics, tend to overlook individual deviations. In this paper, we propose Sysurv, a fully differentiable, non-parametric method that leverages random survival forests to learn individual survival curves, automatically learns conditions and how to combine these into inherently interpretable rules, so as to select 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.

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