在诊断前进行推理：受医生启发的结构化思维用于心电图分类
Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification
- City University of Hong Kong（香港城市大学）
- Beijing Institute of Technology（北京理工大学）
- Shenzhen MSU-BIT University（深圳MSU-BIT大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出CardioThink框架，通过结构化推理过程提升心电图分类的临床相关性，并引入SSPO方法以优化诊断结果的准确性和可解释性。
AI中文摘要：
心电图（ECG）临床诊断依赖于对多个层次方面的结构化推理，包括心律、传导特性、波形形态和总体诊断印象。然而，现有大多数方法直接从ECG信号预测标签，缺乏显式的临床推理过程，导致决策不透明且不具临床相关性。为弥合这一差距，我们提出CardioThink，一个受医生启发的多模态大语言模型（MLLM）框架，通过可解释的中间阶段（心律、传导、形态和印象）显式建模诊断推理过程，以推导最终分类结果。此外，我们引入结构化集合策略优化（SSPO）以联合优化对这种结构化推理格式的遵循程度和变量大小诊断集的准确性，而无需手动标注的推理轨迹。在多样化的ECG基准测试中，广泛实验表明，我们的方法在诊断准确性上显著优于现有方法，同时提供可解释的临床推理。值得注意的是，推理质量评估确认SSPO显著增强了生成的推理依据的临床有效性。这些发现表明，超越直接标签预测，转向结构化推理为未来ECG建模提供了更符合临床需求的方向。
英文摘要：
Electrocardiogram (ECG) diagnosis in clinical practice relies on structured reasoning over multiple hierarchical aspects, including cardiac rhythm, conduction properties, waveform morphology, and overall diagnostic impression. However, most existing approaches predict labels directly from ECG signals without explicit clinical reasoning, resulting in opaque decisions that lack clinical alignment. To bridge this gap, we propose CardioThink, a physician-inspired multimodal large language model (MLLM) framework that explicitly models the diagnostic reasoning process through human-interpretable intermediate stages (rhythm, conduction, morphology, and impression) to derive final classification results. Furthermore, we introduce Structured Set Policy Optimization (SSPO) to jointly optimize adherence to this structured reasoning format and the accuracy of variable-size diagnostic sets, without requiring manually annotated reasoning traces. Extensive experiments on diverse ECG benchmarks demonstrate the significant superiority of our approach in diagnostic accuracy, while simultaneously providing interpretable clinical reasoning. Notably, reasoning quality evaluations confirm that SSPO substantially enhances the clinical validity of the generated rationales. These findings reveal that moving beyond direct label prediction toward structured reasoning offers a more clinically aligned direction for future ECG modeling.
