TRACE：专家对齐的心电图表示学习，在急性心脏护理中进行严格基准测试和真实世界验证
TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care
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中文总结 AI 辅助
TRACE是一种多模态心电图表示模型，通过混合架构和LLM提取报告，在心律失常和结构异常基准上表现优异，并在急性冠状动脉闭塞中比临床实践灵敏度提高19.0%。
中文摘要 AI 辅助
TRACE（基于文本增强的心电图分析）是一种多模态心电图（ECG）表示模型，学习临床基础的信号嵌入以用于下游心脏分类。它旨在解决现有CLIP风格训练的局限性，这些训练常常难以处理嘈杂的临床文本，并且未能利用单模态（来自ECG）和跨模态（ECG与匹配的心脏病专家报告之间）学习的互补优势。为了弥合这一差距，我们提出了一种混合架构，通过不确定性加权的多任务学习联合学习单模态和跨模态表示，同时利用基于LLM的流程从心脏病专家报告中提取高保真发现。我们在临床紧急程度的范围内评估TRACE，在心律失常分类和结构异常的公共基准上相对于现有的单模态和多模态ECG模型建立了稳健的性能。为了展示真实世界的实用性，我们进一步在急性冠状动脉闭塞（ACO）上验证了模型，其中流行的ST段抬高标准漏掉了25-34%的真实闭塞。利用带有专家标注金标准的大型私有ACO数据集，TRACE显著优于真实世界的临床实践，在临床基线上灵敏度提高了19.0%，或假阳性率降低了62.6%。这项广泛的评估证实，TRACE在基准任务上表现出色，并在最急性、高风险的心脏场景中产生切实的临床影响。
英文摘要
TRACE (Text-Reinforced Analysis of Cardio ECGs) is a multimodal electrocardiogram (ECG) representation model that learns clinically grounded signal embeddings for downstream cardiac classification. It is designed to address the limitations of existing CLIP-style training, which often struggles with noisy clinical text and fails to leverage the complementary strengths of unimodal (from ECG) and cross-modal (between ECG and matched cardiologist reports) learning. To bridge this gap, we propose a hybrid architecture that jointly learns unimodal and cross-modal representations via uncertainty-weighted multi-task learning while utilizing an LLM-based pipeline to extract high-fidelity findings from cardiologist reports. We evaluate TRACE across a spectrum of clinical urgency, establishing robust performance on public benchmarks for arrhythmia classification and structural abnormalities relative to existing unimodal and multimodal ECG models. To demonstrate real-world utility, we further validate the model on acute coronary occlusion (ACO), where the prevailing ST-elevation criteria miss 25-34% of true occlusions. Utilizing a large private ACO dataset with expert-annotated ground truth, TRACE significantly outperforms real-world clinical practice, yielding a 19.0% increase in sensitivity or a 62.6% reduction in false positive rates at the clinical baseline. This extensive evaluation confirms that TRACE delivers both strong performance on benchmark tasks and tangible clinical impact in the most acute, high-risk cardiac scenarios.
发表机构
- Emory University（埃默里大学）
- Massachusetts Institute of Technology（麻省理工学院）
- University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
- University of Rochester（罗切斯特大学）
机构由 AI 辅助整理，请以论文原文为准。
