Predicting cardiovascular disease has been a challenging task, as assessing samples based on a single view of information may be insufficient. Therefore, in this paper, we focus on the challenge of predicting cardiovascular disease using multi-view cardiac data. However, multi-view cardiac data is usually difficult to collect and label. Based on this motivation, learning an effective predictive model on sample-efficient and label-efficient multi-view cardiac data is urgently needed. To address the aforementioned issues, we propose a multi-view learning method: (i) our method utilizes graph learning to establish and extract relationships between data, enabling learning from a small number of labeled data and a small number of samples; (ii) our method integrates features from multiple views to utilize complementary information in the data; (iii) for data without a provided graph of relationships between samples, we utilize the mechanism of transformers to learn the relationships between samples in a data-driven manner. We validate the effectiveness of our method on real heart disease datasets.

中文翻译：

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使用图形转换器学习样本高效和标签高效的多视图心脏数据

预测心血管疾病一直是一项具有挑战性的任务，因为基于单一信息视图评估样本可能是不够的。因此，在本文中，我们重点关注使用多视图心脏数据预测心血管疾病的挑战。然而，多视图心脏数据通常难以收集和标记。基于这种动机，迫切需要学习一种有效的样本有效和标签有效的多视图心脏数据预测模型。为了解决上述问题，我们提出了一种多视图学习方法：（i）我们的方法利用图学习来建立和提取数据之间的关系，从而能够从少量标记数据和少量样本中进行学习； (ii) 我们的方法集成了多个视图的特征，以利用数据中的补充信息； (iii)对于没有提供样本之间关系图的数据，我们利用变压器机制以数据驱动的方式学习样本之间的关系。我们在真实的心脏病数据集上验证了我们的方法的有效性。