Recent advancements in Large Language Models (LLMs) have drawn increasing attention since the learned embeddings pretrained on large-scale datasets have shown powerful ability in various downstream applications. However, whether the learned knowledge by LLMs can be transferred to clinical cardiology remains unknown. In this work, we aim to bridge this gap by transferring the knowledge of LLMs to clinical Electrocardiography (ECG). We propose an approach for cardiovascular disease diagnosis and automatic ECG diagnosis report generation. We also introduce an additional loss function by Optimal Transport (OT) to align the distribution between ECG and language embedding. The learned embeddings are evaluated on two downstream tasks: (1) automatic ECG diagnosis report generation, and (2) zero-shot cardiovascular disease detection. Our approach is able to generate high-quality cardiac diagnosis reports and also achieves competitive zero-shot classification performance even compared with supervised baselines, which proves the feasibility of transferring knowledge from LLMs to the cardiac domain.

翻译：近年来，大型语言模型（LLMs）的快速发展引起了广泛关注，因为在大规模数据集上预训练所获得的嵌入表示在多种下游应用中展现出强大的能力。然而，LLMs学习到的知识是否能够迁移到临床心脏病学领域仍是未知数。在本研究中，我们旨在通过将LLMs的知识迁移到临床心电图（ECG）领域来填补这一空白。我们提出了一种用于心血管疾病诊断和自动心电图诊断报告生成的方法。同时，引入了一种基于最优传输（OT）的额外损失函数，以对齐心电图嵌入与语言嵌入之间的分布。学习到的嵌入在两项下游任务中得到了评估：（1）自动心电图诊断报告生成，以及（2）零样本心血管疾病检测。我们的方法能够生成高质量的心脏诊断报告，并且即使与有监督基线方法相比，也取得了具有竞争力的零样本分类性能，这证明了将知识从LLMs迁移到心脏领域的可行性。