心电图数据的自动化医学报告生成：利用深度学习架起医学文本与信号处理的桥梁
Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning
AI总结：
本研究将深度学习驱动的图像描述生成技术应用于心电图数据，提出基于编码器-解码器的方法，利用医疗专业人员撰写的报告训练模型生成类临床医生解读，性能显著优于现有模型，源代码公开且具临床应用潜力。
摘要：
深度学习和自然语言生成的最新进展显著提升了图像描述生成技术，能够为视觉内容生成自动化、类人化的描述。本研究将这些描述生成技术应用于生成类临床医生的心电图（ECG）数据解读。研究利用现有附带医疗专业人员（HCPs）撰写的自由文本报告的ECG数据集作为训练数据，尽管这些报告往往不一致，但为自动化学习提供了宝贵基础。我们提出一种基于编码器-解码器的方法，利用这些报告训练模型生成ECG片段的详细描述，这代表了ECG分析自动化的重大进步，在零样本分类和自动化临床决策支持方面具有潜在应用。模型在多种数据集上进行测试，包括1导联和12导联ECG，其性能显著优于Qiu等人提出的最先进参考模型，METEOR评分达55.53%，而参考模型为24.51%。此外，还讨论了几个关键设计选择，全面概述了该领域当前的挑战与创新。本研究的源代码公开于我们的Git仓库：https://git.zib.de/ableich/ecg-comment-generation-public
英文摘要：
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
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。
