用对比学习提升日文心电图分类，98类准确率媲美英文研究。
Application of Contrastive Learning on ECG Data: Evaluating Performance in Japanese and Classification with Around 100 Labels
- 基于对比学习框架，结合日文标签构建多模态模型。
- 在98个日本临床标签上实现与英文研究相当的分类准确率。
- 推动非英语医疗文本分析落地，适合临床辅助诊断场景。
心电图（ECG）是心血管疾病诊断的重要无创工具，其关键用途之一是判断是否需要进一步检查，用户涵盖不同专业水平。为降低误判风险，近年来机器学习方法致力于从ECG数据中提取有效信息。已有研究通过语言模型构建多模态模型，对心电图进行标签分类，但通常限制类别数量，且未验证在非英语语言中的有效性。本研究采用日本医院常规患者的心电图数据，保留实际读数中获得的大量日文标签（共98类），并基于对比学习框架训练模型。结果表明，该模型在98类分类任务上的表现可媲美先前英文研究。本工作拓展了多模态机器学习在更广泛临床研究和非英语语言中的适用性。
原文摘要 · Abstract (English)
The electrocardiogram (ECG) is a fundamental tool in cardiovascular diagnostics due to its powerful and non-invasive nature. One of the most critical usages is to determine whether more detailed examinations are necessary, with users ranging across various levels of expertise. Given this diversity in expertise, it is essential to assist users to avoid critical errors. Recent studies in machine learning have addressed this challenge by extracting valuable information from ECG data. Utilizing language models, these studies have implemented multimodal models aimed at classifying ECGs according to labeled terms. However, the number of classes was reduced, and it remains uncertain whether the technique is effective for languages other than English. To move towards practical application, we utilized ECG data from regular patients visiting hospitals in Japan, maintaining a large number of Japanese labels obtained from actual ECG readings. Using a contrastive learning framework, we found that even with 98 labels for classification, our Japanese-based language model achieves accuracy comparable to previous research. This study extends the applicability of multimodal machine learning frameworks to broader clinical studies and non-English languages.
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