对比一切：面向医疗时间序列的分层对比框架
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
AI总结：
针对现有医疗时间序列对比学习方法仅聚焦单一层级的问题，提出分层框架COMET，从四个层级捕获数据一致性并构建多层级对比损失，在患者独立设置下的多数据集实验中优于基线模型，提升了自监督表示学习效果。
摘要：
对比表示学习在医疗时间序列分析中至关重要，因为它能减轻对劳动密集型、领域特定且稀缺的专家标注的依赖。然而，现有对比学习方法主要聚焦于单一数据层级，无法充分挖掘医疗时间序列的复杂特性。为解决这一问题，我们提出COMET（一种创新的分层框架），该框架利用医疗时间序列所有固有层级的数据一致性。我们精心设计的模型系统地从四个潜在层级捕获数据一致性：观测层、样本层、试验层和患者层。通过在多个层级构建对比损失，我们能够学习到保留全面数据一致性的有效表示，以自监督的方式最大化信息利用率。我们在具有挑战性的患者独立设置下开展实验，使用三个多样化的数据集（包括心肌梗死的ECG信号、阿尔茨海默病和帕金森病的EEG信号），将COMET与六个基线模型进行对比。结果表明，COMET始终优于所有基线，尤其是在所有数据集的10%和1%标注数据比例设置下表现更突出。这些结果凸显了我们的框架在推动医疗时间序列对比表示学习技术发展方面的重要作用。源代码可在https://github.com/DL4mHealth/COMET 获取。
英文摘要：
Contrastive representation learning is crucial in medical time series analysis as it alleviates dependency on labor-intensive, domain-specific, and scarce expert annotations. However, existing contrastive learning methods primarily focus on one single data level, which fails to fully exploit the intricate nature of medical time series. To address this issue, we present COMET, an innovative hierarchical framework that leverages data consistencies at all inherent levels in medical time series. Our meticulously designed model systematically captures data consistency from four potential levels: observation, sample, trial, and patient levels. By developing contrastive loss at multiple levels, we can learn effective representations that preserve comprehensive data consistency, maximizing information utilization in a self-supervised manner. We conduct experiments in the challenging patient-independent setting. We compare COMET against six baselines using three diverse datasets, which include ECG signals for myocardial infarction and EEG signals for Alzheimer's and Parkinson's diseases. The results demonstrate that COMET consistently outperforms all baselines, particularly in setup with 10% and 1% labeled data fractions across all datasets. These results underscore the significant impact of our framework in advancing contrastive representation learning techniques for medical time series. The source code is available at https://github.com/DL4mHealth/COMET.
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。
