CardioRAG：一种用于多模态恰加斯病检测的检索增强生成框架
CardioRAG: A Retrieval-Augmented Generation Framework for Multimodal Chagas Disease Detection
AI总结：
针对现有恰加斯病AI检测方法准确率不足、依赖标注数据、临床指标融合弱的问题，提出融合大语言模型与可解释心电图特征的检索增强生成框架CardioRAG，实现高召回率的阳性病例识别，为资源有限地区提供循证可信的医疗AI方案。
摘要：
全球约有600万人感染恰加斯病，其中恰加斯心肌病是其最严重的并发症。在血清学检测能力有限的地区，人工智能增强的心电图（ECG）筛查是重要的诊断替代方案。然而，现有机器学习方法存在准确率有限、依赖大量标注数据集的问题，更关键的是与循证临床诊断指标的融合度较弱。我们提出了检索增强生成框架CardioRAG，将大语言模型与可解释的心电图临床特征（包括右束支传导阻滞、左前分支阻滞和心率变异性指标）相结合。该框架使用变分自编码器学习到的表征进行语义病例检索，提供上下文病例以指导临床推理。评估显示，该框架的召回率高达89.80%，最大F1值为0.68，可有效识别需要优先进行血清学检测的阳性病例。CardioRAG提供了一种可解释、基于临床证据的方法，对资源有限的环境尤其有价值，为将临床指标嵌入可信医疗人工智能系统提供了路径。
英文摘要：
Chagas disease affects nearly 6 million people worldwide, with Chagas cardiomyopathy representing its most severe complication. In regions where serological testing capacity is limited, AI-enhanced electrocardiogram (ECG) screening provides a critical diagnostic alternative. However, existing machine learning approaches face challenges such as limited accuracy, reliance on large labeled datasets, and more importantly, weak integration with evidence-based clinical diagnostic indicators. We propose a retrieval-augmented generation framework, CardioRAG, integrating large language models with interpretable ECG-based clinical features, including right bundle branch block, left anterior fascicular block, and heart rate variability metrics. The framework uses variational autoencoder-learned representations for semantic case retrieval, providing contextual cases to guide clinical reasoning. Evaluation demonstrated high recall performance of 89.80%, with a maximum F1 score of 0.68 for effective identification of positive cases requiring prioritized serological testing. CardioRAG provides an interpretable, clinical evidence-based approach particularly valuable for resource-limited settings, demonstrating a pathway for embedding clinical indicators into trustworthy medical AI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
