# Discovering Subgroups with Exceptional Survival Characteristics

arXiv: https://arxiv.org/abs/2602.22179 · HTML: https://mlsanity.com/paper/2602.22179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 46 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Causal machine learning for predicting treatment outcomes](https://mlsanity.com/paper/2410.08770/) (2024)
- [Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework](https://mlsanity.com/paper/1801.05512/) (2018)
- [Deep Cox Mixtures for Survival Regression](https://mlsanity.com/paper/2101.06536/) (2021)
- [Survival regression with accelerated failure time model in XGBoost](https://mlsanity.com/paper/2006.04920/) (2020)
- [A Deep Variational Approach to Clustering Survival Data](https://mlsanity.com/paper/2106.05763/) (2021)
- [SurvSet: An open-source time-to-event dataset repository](https://mlsanity.com/paper/2203.03094/) (2022)
- [Optimal Survival Trees](https://mlsanity.com/paper/2012.04284/) (2020)
- [DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT](https://mlsanity.com/paper/2109.07711/) (2021)
- [SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast](https://mlsanity.com/paper/2403.10603/) (2024)
- [Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer](https://mlsanity.com/paper/2307.03427/) (2023)
- [Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery](https://mlsanity.com/paper/1701.07696/) (2017)
- [RuleKit: A Comprehensive Suite for Rule-Based Learning](https://mlsanity.com/paper/1908.01031/) (2019)
- [Optimal Survival Trees: A Dynamic Programming Approach](https://mlsanity.com/paper/2401.04489/) (2024)
- [Optimal Sparse Survival Trees](https://mlsanity.com/paper/2401.15330/) (2024)
- [Learning Exceptional Subgroups by End-to-End Maximizing KL-divergence](https://mlsanity.com/paper/2402.12930/) (2024)
- [Survival Prediction in Lung Cancer through Multi-Modal Representation Learning](https://mlsanity.com/paper/2409.20179/) (2024)
- [EsmamDS: A more diverse exceptional survival model mining approach](https://mlsanity.com/paper/2109.02610/) (2021)

## Abstract (arXiv)

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.