Learning and Naming Subgroups with Exceptional Survival Characteristics
2026-02-25 • Machine Learning
Machine Learning
AI summaryⓘ
The authors present Sysurv, a new method to find groups of individuals who live longer or shorter than others, which is important in fields like medicine and maintenance. Unlike older methods, Sysurv does not rely on strict assumptions about survival patterns or simplified data, and it looks at individual survival chances instead of just averages. It uses a combination of random survival forests and interpretable rules to automatically identify meaningful subgroups. Tests on various datasets, including cancer cases, show that Sysurv can find useful groups with different survival outcomes.
survival analysissubpopulationsrandom survival forestsnon-parametric methodsproportional hazardssurvival curvesinterpretable rulespredictive maintenancecancer survival
Authors
Mhd Jawad Al Rahwanji, Sascha Xu, Nils Philipp Walter, Jilles Vreeken
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.