用深度学习自动生成类医生的心电图报告，提升医疗自动化水平。
Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning
- 采用编码器-解码器结构，结合医生撰写的自由文本报告训练模型。
- 在多数据集上表现超越现有最佳模型，METEOR得分达55.53%。
- 代码开源，适合医疗AI研究者和临床辅助系统开发者参考。
深度学习与自然语言生成技术的进步显著提升了图像描述生成能力，使视觉内容可自动生成类人描述。本文将此类技术应用于心电图（ECG）数据，旨在生成类医生的解读报告。研究利用附带专业医护人员（HCPs）撰写自由文本报告的心电图数据集作为训练数据，尽管这些报告存在不一致性，但为自动化学习提供了宝贵基础。提出一种基于编码器-解码器的生成方法，训练模型对心电图事件生成详细描述。该工作推动了心电图分析自动化，具备零样本分类与自动临床决策支持的应用潜力。模型在1导联与12导联心电图数据集上均测试，显著优于参考模型（Qiu等），METEOR得分由24.51%提升至55.53%。同时探讨多个关键设计选择，系统梳理当前领域挑战与创新。相关源码已公开于https://git.zib.de/ableich/ecg-comment-generation-public。
原文摘要 · Abstract (English)
Recent advances in deep learning and natural language generation have significantly improved image captioning, enabling automated, human-like descriptions for visual content. In this work, we apply these captioning techniques to generate clinician-like interpretations of ECG data. This study leverages existing ECG datasets accompanied by free-text reports authored by healthcare professionals (HCPs) as training data. These reports, while often inconsistent, provide a valuable foundation for automated learning. We introduce an encoder-decoder-based method that uses these reports to train models to generate detailed descriptions of ECG episodes. This represents a significant advancement in ECG analysis automation, with potential applications in zero-shot classification and automated clinical decision support. The model is tested on various datasets, including both 1- and 12-lead ECGs. It significantly outperforms the state-of-the-art reference model by Qiu et al., achieving a METEOR score of 55.53% compared to 24.51% achieved by the reference model. Furthermore, several key design choices are discussed, providing a comprehensive overview of current challenges and innovations in this domain. The source codes for this research are publicly available in our Git repository https://git.zib.de/ableich/ecg-comment-generation-public
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