首个能预测心律失常的心电图语言模型，支持长时序推理。
CAMEL: An ECG Language Model for Forecasting Cardiac Events
- 设计专用编码器实现心电图与文本跨模态理解。
- 在6项任务9个数据集上零样本表现领先，最高提升21.1%。
- 适合临床预警系统研发者及心血管AI研究者使用。
心电图（ECG）是诊断心血管疾病的关键电生理记录。近年来，心电图语言模型（ELM）在分类与报告生成方面展现出潜力，但尚无法预测未来心脏事件，而这一能力对早期干预具有重大临床价值。为此，我们提出CAMEL，首个具备长时序信号推理能力的ELM，从而实现预测功能。核心思路是设计专用的ECG编码器，实现心电图与文本的跨模态理解。采用标准大语言模型训练流程，结合LoRA微调与课程学习策略，课程包含分类、指标计算和多轮对话以激发模型推理能力。CAMEL在6个任务和9个数据集上展现强零样本性能，包括我们新提出的ECGForecastBench基准，用于心律失常预测。其性能达到或超越现有ELM及全监督基线，在ECGBench上平均绝对提升7.0%，在ECGForecastBench上较全监督模型提升12.4%，较零样本ELM提升21.1%。
原文摘要 · Abstract (English)
Electrocardiograms (ECG) are electrical recordings of the heart that are critical for diagnosing cardiovascular conditions. ECG language models (ELMs) have recently emerged as a promising framework for ECG classification accompanied by report generation. However, current models cannot forecast future cardiac events despite the immense clinical value for planning earlier intervention. To address this gap, we propose CAMEL, the first ELM that is capable of inference over longer signal durations which enables its forecasting capability. Our key insight is a specialized ECG encoder which enables cross-understanding of ECG signals with text. We train CAMEL using established LLM training procedures, combining LoRA adaptation with a curriculum learning pipeline. Our curriculum includes ECG classification, metrics calculations, and multi-turn conversations to elicit reasoning. CAMEL demonstrates strong zero-shot performance across 6 tasks and 9 datasets, including ECGForecastBench, a new benchmark that we introduce for forecasting arrhythmias. CAMEL is on par with or surpasses ELMs and fully supervised baselines both in- and out-of-distribution, achieving SOTA results on ECGBench (+7.0% absolute average gain) as well as ECGForecastBench (+12.4% over fully supervised models and +21.1% over zero-shot ELMs).
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