ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression

• URL: http://arxiv.org/abs/2509.07108v1
• Date: Mon, 08 Sep 2025 18:04:14 GMT
• Title: ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression
• Authors: Mert Ketenci, Vincent Jeanselme, Harry Reyes Nieva, Shalmali Joshi, Noémie Elhadad,
• Abstract summary: Survival analysis is a fundamental tool for modeling time-to-event outcomes in healthcare.<n>We propose Additive Deep Hazard Analysis Mixtures (ADHAM), an interpretable additive survival model.<n>We perform comprehensive studies to demonstrate ADHAM's interpretability at the population, subgroup, and individual levels.
• Score: 6.7535930849356225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Survival analysis is a fundamental tool for modeling time-to-event outcomes in healthcare. Recent advances have introduced flexible neural network approaches for improved predictive performance. However, most of these models do not provide interpretable insights into the association between exposures and the modeled outcomes, a critical requirement for decision-making in clinical practice. To address this limitation, we propose Additive Deep Hazard Analysis Mixtures (ADHAM), an interpretable additive survival model. ADHAM assumes a conditional latent structure that defines subgroups, each characterized by a combination of covariate-specific hazard functions. To select the number of subgroups, we introduce a post-training refinement that reduces the number of equivalent latent subgroups by merging similar groups. We perform comprehensive studies to demonstrate ADHAM's interpretability at the population, subgroup, and individual levels. Extensive experiments on real-world datasets show that ADHAM provides novel insights into the association between exposures and outcomes. Further, ADHAM remains on par with existing state-of-the-art survival baselines in terms of predictive performance, offering a scalable and interpretable approach to time-to-event prediction in healthcare.

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