A censoring-aware target interface for tabular foundation models in survival prediction

• URL: http://arxiv.org/abs/2607.09577v1
• Date: Fri, 10 Jul 2026 16:26:19 GMT
• Title: A censoring-aware target interface for tabular foundation models in survival prediction
• Abstract summary: We introduce SurvFM-RMST, a censoring-aware target-interface framework.<n>It converts survival outcomes into jackknife pseudo-observation targets for restricted mean survival time.<n>In simulations with known conditional RMST, SurvFM-RMST recovered restricted event-free time accurately.
• Score: 1.402135576373209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time-to-event prediction from tabular patient data is central to prognosis and biomedical decision support, but right-censored follow-up prevents direct use of ordinary regression labels. Tabular foundation models offer reusable prediction machinery for modest heterogeneous datasets, yet they generally assume fully observed outcomes. We introduce SurvFM-RMST, a censoring-aware target-interface framework that converts survival outcomes into jackknife pseudo-observation targets for restricted mean survival time, enabling multiple tabular backbones to perform horizon-specific RMST regression without survival-specific fine-tuning. In controlled simulations with known conditional RMST, SurvFM-RMST recovered restricted event-free time accurately, and pseudo-RMST targets outperformed naive restricted observed-time and event-only targets. Across 36 eligible static SurvSet datasets, SurvFM backbones were competitive with established survival and RMST-regression comparators, though relative performance varied by endpoint, horizon and practical constraints. Predicted RMST further stratified held-out patients into groups with ordered observed event-free time and event enrichment. Overall, the results support pseudo-RMST target construction as a portable interface between censored survival data and tabular foundation-model prediction.

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