用知识增强的多模态方法，让心电图在任意导联下也能精准分析。
Knowledge-enhanced Multimodal ECG Representation Learning with Arbitrary-Lead Inputs
- 用大模型从报告中提取结构化知识，结合动态导联掩码处理任意导联输入。
- 在部分导联条件下零样本分类，平均AUC提升16%。
- 适合资源有限场景下的心电图智能诊断，尤其对导联不全数据有效。
近期多模态心电图表征学习聚焦于对齐心电图信号与配对自由文本报告。然而，由于医学语言复杂性以及对完整12导联设置的依赖，对齐效果仍不理想，尤其在资源匮乏环境中难以实现。为此，我们提出**K-MERL**——一种知识增强的多模态心电图表征学习框架。该框架利用大语言模型从自由文本报告中提取结构化知识，并采用具有导联感知能力的心电图编码器与动态导联掩码机制，以支持任意导联输入。在六个外部心电图数据集上的评估表明，**K-MERL**在零样本分类和线性探测任务中均达到当前最优性能，在部分导联零样本分类中平均AUC相比现有方法提升16%。
原文摘要 · Abstract (English)
Recent advances in multimodal ECG representation learning center on aligning ECG signals with paired free-text reports. However, suboptimal alignment persists due to the complexity of medical language and the reliance on a full 12-lead setup, which is often unavailable in under-resourced settings. To tackle these issues, we propose **K-MERL**, a knowledge-enhanced multimodal ECG representation learning framework. **K-MERL** leverages large language models to extract structured knowledge from free-text reports and employs a lead-aware ECG encoder with dynamic lead masking to accommodate arbitrary lead inputs. Evaluations on six external ECG datasets show that **K-MERL** achieves state-of-the-art performance in zero-shot classification and linear probing tasks, while delivering an average **16%** AUC improvement over existing methods in partial-lead zero-shot classification.
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