用分层推理提升心脏病术后风险预测的可解释性
CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis
- 分两阶段：先生成医学影像的逻辑推理路径，再融合预测
- 在MACE风险预测上表现优于现有方法，且推理过程可读
- 适合需要可解释性医疗决策的临床医生和研究者
基于术后心脏磁共振成像和临床记录，准确预测急性心肌梗死患者主要不良心血管事件（MACE）复发风险，对精准治疗和个性化干预至关重要。现有方法多关注风险分层能力，忽视临床实践中对中间稳健推理和模型可解释性的需求。此外，使用大语言模型/视觉语言模型（LLM/VLM）进行端到端预测面临数据有限和建模复杂等挑战。为此，我们提出CardioCoT——一种新型两阶段分层推理增强型生存分析框架，旨在提升模型可解释性与预测性能。第一阶段，采用证据增强的自精炼机制，引导LLM/VLM根据影像学发现生成稳健的分层推理路径；第二阶段，将推理路径与影像数据结合用于风险模型训练与预测。CardioCoT在MACE复发风险预测中表现优异，并提供可解释的推理过程，为临床决策提供重要支持。
原文摘要 · Abstract (English)
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.
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