Adaptation Interfaces for In-Context Tabular Foundation Models in Time-to-Event Prediction

2026-09-04

AI Summary

研究通过改进TabFMs与CoxPH和DeepHit的结合方法，解决了结构化数据中被删失的时间到事件预测问题，并评估了不同适应接口的有效性。

Abstract

Tabular foundation models (TabFMs) achieve strong performance on structured data, particularly for standard classification and regression problems. Yet, extending them to censored time-to-event prediction is challenging because it requires properly handling censoring and event-time dynamics. Building on our prior work, we further link TabFMs with CoxPH and DeepHit and revise the context-resampled training procedure. We evaluate temporal zero-shot reformulation, classification-based fine-tuning, and survival-head adaptation using frozen TabFM backbones on 74 single-risk data sets, and we additionally study 4 competing-risk data sets. Zero-shot inference is effective on smaller single-risk data sets, whereas supervised adaptation becomes increasingly advantageous as data sets scale. Cox provides the most reliably strong interface, especially for Integrated Brier Score (IBS) on larger data sets. DeepHit is relatively stronger for the time-dependent Concordance Index than for IBS, while cause-specific MTLR ranks highest among the TabFM survival heads in the four-data-set competing-risk analysis. Classification fine-tuning becomes more competitive with zero-shot inference as data sets grow but remains weaker for probabilistic prediction. Overall, our results indicate that effective TabFM transfer depends on the data regime and on the statistical structure represented by the chosen adaptation interface. The implementation scripts used for this work are available at https://github.com/kaylode/survival-fm.

Problem

• Tabular foundation models
• time-to-event prediction
• censoring
• event-time dynamics

Innovation

• Tabular Foundation Models
• Censored Time-to-Event Prediction
• Context-Resampled Training
• Survival Analysis
• Adaptation Interfaces