Towards end-to-end LLM-based censoring-aware survival analysis
Objective: Survival analysis is central to medical prediction, yet large language models (LLMs) are rarely used as end-to-end survival models because censoring prevents straightforward supervised fine-tuning. Here we present LLMSurvival, a framework that enables censoring-aware survival analysis with unmodified LLMs operating directly on tabular clinical data. Materials and Methods: LLMSurvival reformulates time-to-event prediction as pairwise ranking among comparable subjects, and derives test-time risk by aggregating comparisons against anchor individuals from the training cohort. Results: Across two clinical tasks (ICU mortality prediction in MIMIC-IV and fragility fracture prediction ...
| Authors | Yishu Wei, Hexin Dong, Yi Lin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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