SurvICL: a Foundation Model for in-context Survival Analysis

Key points

• Tabular foundation models (TFMs) now outperform gradient-boosted trees in classification and regression.1
• Yet in survival analysis, which predicts when an event occurs from censored data, often on small clinical cohorts, we find that no existing TFM beats the half-century-old Cox model.1
• SurvICL is pretrained on purely synthetic datasets generated from a novel flexible proportional-odds prior that outperforms other prior types in our ablation study.1
• Additionally, we introduce the largest survival benchmark to date spanning 103 datasets.1

Sources (1)

• [1]SurvICL: a Foundation Model for in-context Survival Analysis
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:16 PM

  We address this issue by introducing SurvICL, a TFM that predicts survival curves in-context.

  Tabular foundation models (TFMs) now outperform gradient-boosted trees in classification and regression.

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