Abstract
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.
Abstract (translated)
近年来,大型语言模型(LLMs)的进展情况吸引了越来越多的关注,因为从大型数据集训练的 learned embeddings 在多种下游应用中表现出了强大的能力。然而,LLMs 学到的知识是否能够转移到临床心脏病学领域仍然未知。在本研究中,我们旨在通过将LLMs 的知识转移到临床心电图(ECG)上来填补这一差距。我们提出了一种心血管疾病诊断和自动ECG诊断报告生成的方法。此外,我们还通过 Optimal Transport(OT)引入了额外的损失函数,以协调ECG和语言Embedding之间的分布。 learned embeddings 将根据两个下游任务进行评估:(1)自动ECG诊断报告生成,(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病学诊断报告,并且即使在与监督基线相比的情况下,也能实现具有竞争力的零样本分类性能,这表明将LLMs 的知识转移到心血管领域的知识传输可行性。
URL
https://arxiv.org/abs/2301.09017
