标题： CardioCoT：多模态生存分析的分层推理 显示英文标题

标题： CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis

摘要： 基于术后心脏MRI和相关临床记录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中间稳健推理和模型可解释性的需求。 此外，使用LLM/VLM进行端到端风险预测面临数据限制和建模复杂性的重大挑战。 为弥补这一差距，我们提出CardioCoT，一种新颖的两阶段分层推理增强生存分析框架，旨在提高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，指导LLM/VLM根据相关的影像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结合进行风险模型训练和预测。 CardioCoT在MACE复发风险预测中表现出色，同时提供可解释的推理过程，为临床决策提供了有价值的见解。 显示英文摘要

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.