LLMs adapted for censoring-aware survival analysis in medicine

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

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

排序理由 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]

在 arXiv cs.AI 阅读 →

• Cox proportional hazards modeling
• large language models
• LLMSurvival
• MIMIC-IV
• NewYork-Presbyterian/Weill Cornell Medicine

• 论文
• 模型发布

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