Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation
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Computer Science > Machine Learning
arXiv:2606.12006 (cs)
[Submitted on 10 Jun 2026]
Title:Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation
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Abstract:Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis. While classical statistical and deep learning approaches have been widely studied, they typically require task-specific training and sufficient labeled data. Recent advances in tabular foundation models offer a new paradigm by learning general-purpose representations for structured data. However, their applicability to censored time-to-event prediction in clinical settings remains underexplored, as typical applications are restricted to discrete classification rather than survival analysis tasks.
In this work, we propose a lightweight adaptation approach for applying tabular foundation models to clinical survival analysis by directly training a survival-aware head on top of the pretrained representations. We study representative architectures, including TabPFN, TabDPT, and TabICL, and adapt them using a multi-task logistic regression (MTLR) head to model right-censored time-to-event outcomes. We evaluate this approach on a diverse set of public survival benchmarks and two large-scale ICU cohorts, MIMIC-IV and eICU.
Our results show that this transfer learning approach achieves competitive or superior performance compared to strong baselines. On MIMIC-IV, TabDPT-FT-MTLR reaches a C-index of 0.856, corresponding to a relative improvement of +1.4% over the best non-FM baseline (DeepSurv, 0.844) and +6.7% over the best zero-shot model (0.802). On eICU, TabICL-FT-MTLR achieves 0.797, yielding gains of +1.7% (DeepSurv, 0.784) and +6.4% (0.749), respectively. These findings highlight the importance of combining pretrained tabular representations with survival-aware objectives and suggest that tabular foundation models provide a practical and effective alternative for clinical survival prediction.
| Comments: | Accepted for publication at International Conference on AI in Healthcare 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.12006 [cs.LG] |
| (or arXiv:2606.12006v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.12006
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation, by Minh-Khoi Pham and 5 other authors
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