MS-ECG-FM：利用多源对比学习实现更通用的心电图基础模型以用于健康监测
MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning
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中文总结 AI 辅助
针对现有心电图基础模型仅依赖解读报告而忽略更广诊断信号的问题，提出MS-ECG-FM，通过多源对比学习对齐多种临床笔记，在扩展基准上全面超越现有方法，实现更通用的健康监测。
中文摘要 AI 辅助
心电图（ECG）记录心脏的电活动，通过检测心脏功能的异常来辅助诊断。心电图基础模型已展现出令人鼓舞的结果，但受限于其仅依赖心电图解读报告作为唯一监督信号。由于解读报告仅捕捉临床医生常规识别的波形信息子集，这限制了表示学习，使其忽略了心电图中存在的更广泛的诊断信号。我们引入了一种新的心电图基础模型——MS-ECG-FM——该模型通过对比对齐训练到多种不同的临床笔记类型，包括心电图、超声心动图、放射学和出院报告。我们在扩展的心电图检测基准集上评估了MS-ECG-FM，结果表明，在心电图可检测的全部病症范围内，包括在减少导联配置下，它全面优于现有方法。不同的报告改善了不同诊断领域的表示，而多源对齐则捕获了它们的互补信息，并在临床多样化的任务中产生了一致的强表示。
英文摘要
Electrocardiography (ECG) records the electrical activity of the heart, aiding diagnosis by detecting abnormalities in cardiac function. ECG foundation models have demonstrated promising results, but are limited by a reliance on ECG interpretation reports as their sole supervision. Because interpretation reports only capture the subset of waveform information routinely recognized by clinicians, this constrains representation learning to overlook the broader diagnostic signals present in ECG. We introduce a new ECG foundation model --- MS-ECG-FM --- that is trained through contrastive alignment to multiple distinct clinical note types, including ECG, echocardiography, radiology, and discharge reports. We evaluate MS-ECG-FM on an extended set of ECG detection benchmarks, showing that it comprehensively outperforms existing methods on the full span of conditions that ECG can detect, including in reduced-lead configurations. Different reports improve representations for different diagnostic domains, while multi-source alignment captures their complementary information and produces consistently strong representations across clinically diverse tasks.
发表机构
- Massachusetts Institute of Technology（麻省理工学院）
- Apple, Inc（苹果公司）
- Johns Hopkins Medicine（约翰斯·霍普金斯医学）
- Princeton University（普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
