用大模型+专家知识库实现高可靠心电图自动诊断
ALFRED: Ask a Large-language model For Reliable ECG Diagnosis
- 基于RAG框架融合专家标注的知识库提升诊断可信度
- 在PTB-XL数据集上实现精准心电图分析，结果可解释性强
- 无需额外训练，适合临床辅助诊断场景
将大型语言模型（LLMs）与检索增强生成（RAG）技术结合用于医疗数据，特别是心电图（ECG）分析，可实现高准确率与便捷性。然而，在医学等专业领域生成可靠、有证据支持的结果仍具挑战，仅靠RAG难以满足要求。本文提出一种基于RAG的零样本心电图诊断框架，引入专家人工整理的知识库以增强诊断准确性和可解释性。在PTB-XL数据集上的评估表明该框架有效，凸显结构化领域知识在自动化心电图解读中的价值。本框架旨在支持全面的心电图分析，能够应对多样化的诊断需求，具备超越测试数据集的应用潜力。
原文摘要 · Abstract (English)
Leveraging Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for analyzing medical data, particularly Electrocardiogram (ECG), offers high accuracy and convenience. However, generating reliable, evidence-based results in specialized fields like healthcare remains a challenge, as RAG alone may not suffice. We propose a Zero-shot ECG diagnosis framework based on RAG for ECG analysis that incorporates expert-curated knowledge to enhance diagnostic accuracy and explainability. Evaluation on the PTB-XL dataset demonstrates the framework's effectiveness, highlighting the value of structured domain expertise in automated ECG interpretation. Our framework is designed to support comprehensive ECG analysis, addressing diverse diagnostic needs with potential applications beyond the tested dataset.
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