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LLMs adapted for censoring-aware survival analysis in medicine

By PulseAugur Editorial · [1 sources] ·

Researchers have developed LLMSurvival, a novel framework that enables the use of large language models for survival analysis in clinical settings. This approach reformulates time-to-event prediction as a pairwise ranking problem, allowing unmodified LLMs to handle censored data. LLMSurvival demonstrated improved performance over traditional Cox models and existing deep learning methods on ICU mortality and fragility fracture prediction tasks. AI

IMPACT Enables LLMs to perform complex medical predictions on censored data, potentially improving clinical decision-making.

RANK_REASON The cluster describes a new research paper introducing a novel framework for survival analysis using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs adapted for censoring-aware survival analysis in medicine

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The cluster describes a new research paper introducing a novel framework for survival analysis using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yishu Wei, Hexin Dong, Yi Lin, Jiahe Qian, Yi Liu, Yifan Peng ·

    Towards end-to-end LLM-based censoring-aware survival analysis

    arXiv:2605.25399v1 Announce Type: new Abstract: 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 LLMSurv…

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