用检索增强生成提升心电图文本模型的准确性与可靠性
Retrieval-Augmented Generation for Electrocardiogram-Language Models
- 将检索增强生成技术引入心电图-文本模型，结合外部知识库
- 在三个公开数据集上，RAG模型显著优于非检索基线
- 首次开源完整RAG流程，适合医疗AI与多模态研究者
生成式心电图-文本模型（ELMs）日益受到关注，因其能根据心电图信号和文本查询生成自然语言回答。与传统分类器输出标签概率不同，ELMs支持领域任务（如波形分析、诊断、预后）和通用任务（如开放问题、对话）。检索增强生成（RAG）广泛用于大语言模型，通过引入检索知识来减少幻觉并提升自然语言生成质量。然而，目前尚无面向ELMs的开源实现或系统性RAG设计研究。为此，我们首次提出面向ELMs的开源RAG管道，包含基线与消融实验。在三个公开数据集上的实验表明，引入RAG的ELM在各项指标上均优于非检索基线，同时揭示了关键的ELM设计要点。代码已公开：https://github.com/willxxy/ECG-Bench。
原文摘要 · Abstract (English)
Interest in generative Electrocardiogram-Language Models (ELMs) is growing, as they can produce textual responses conditioned on ECG signals and textual queries. Unlike traditional classifiers that output label probabilities, ELMs are more versatile, supporting domain-specific tasks (e.g., waveform analysis, diagnosis, prognosis) as well as general tasks (e.g., open-ended questions, dialogue). Retrieval-Augmented Generation (RAG), widely used in Large Language Models (LLMs) to ground LLM outputs in retrieved knowledge, helps reduce hallucinations and improve natural language generation (NLG). However, despite its promise, no open-source implementation or systematic study of RAG pipeline design for ELMs currently exists. To address this gap, we present the first open-source RAG pipeline for ELMs, along with baselines and ablation studies for NLG. Experiments on three public datasets show that ELMs with RAG consistently improves performance over non-RAG baselines and highlights key ELM design considerations. Our code is available at: https://github.com/willxxy/ECG-Bench.
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