CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis
This addresses the need for interpretable and accurate risk prediction in clinical practice for acute myocardial infarction patients, representing a novel method for a known bottleneck.
The paper tackled the problem of predicting major adverse cardiovascular events recurrence risk in acute myocardial infarction patients using postoperative cardiac MRI and clinical notes, achieving superior performance in risk prediction while providing interpretable reasoning processes.
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.