CardioCoT：多模态生存分析的分层推理
CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis
AI总结：
针对急性心肌梗死患者术后MACE复发风险预测，提出CardioCoT两阶段分层推理框架，结合证据增强自精炼机制与影像数据，提升可解释性与预测性能。
摘要：
基于术后心脏磁共振成像和相关临床笔记，准确预测急性心肌梗死患者主要不良心血管事件复发风险，对于精准治疗和个性化干预至关重要。现有方法主要关注风险分层能力，而忽视了临床实践中对中间稳健推理和模型可解释性的需求。此外，由于数据限制和建模复杂性，使用LLM/VLM进行端到端的风险预测面临重大挑战。为弥补这一差距，我们提出了CardioCoT，一种新颖的两阶段分层推理增强的生存分析框架，旨在增强模型的可解释性和预测性能。在第一阶段，我们采用证据增强的自我精炼机制，引导LLM/VLM基于相关放射学发现生成稳健的分层推理轨迹。在第二阶段，我们将推理轨迹与影像数据整合，用于风险模型训练和预测。CardioCoT在MACE复发风险预测中展现出优越性能，同时提供可解释的推理过程，为临床决策提供有价值的见解。
英文摘要：
Accurate prediction of major adverse cardiovascular events recurrence risk in acute myocardial infarction patients based on postoperative cardiac MRI and associated clinical notes is crucial for precision treatment and personalized intervention. Existing methods primarily focus on risk stratification capability while overlooking the need for intermediate robust reasoning and model interpretability in clinical practice. Moreover, end-to-end risk prediction using LLM/VLM faces significant challenges due to data limitations and modeling complexity. To bridge this gap, we propose CardioCoT, a novel two-stage hierarchical reasoning-enhanced survival analysis framework designed to enhance both model interpretability and predictive performance. In the first stage, we employ an evidence-augmented self-refinement mechanism to guide LLM/VLMs in generating robust hierarchical reasoning trajectories based on associated radiological findings. In the second stage, we integrate the reasoning trajectories with imaging data for risk model training and prediction. CardioCoT demonstrates superior performance in MACE recurrence risk prediction while providing interpretable reasoning processes, offering valuable insights for clinical decision-making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
