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Electrocardiogram (ECG) measurements are essential for detecting and treating cardiovascular disease (CVD). However, manual evaluation of ECGs is prone to errors due to morphological variations. Although machine learning methods have shown promise in diagnosing diseases, automatic CVD diagnosis based on ECGs is still suffering from low diagnosis accuracy due to the limited usage of time-series information and inter-lead correlations. In this paper, we propose a large language model-assisted graph attentive feature interaction learning framework (CVDLLM) for automatic ECG diagnosis. It utilizes ECG data from twelve leads to classify eight heart diseases, including rhythm abnormalities and normal conditions. Our framework combines convolutional and recurrent neural networks for independent time-series feature extraction from 12-lead ECG signals. By incorporating features extracted by heart rate variability (HRV) analysis, we employ graph attention neural networks (GAT) and self-attentive feature interaction mechanism (GSAT) for feature interaction and model learning. Leveraging large language models (LLMs) with pre-trained knowledge bases and advanced language comprehension, we extract and learn semantic embeddings from medical case data. This approach equips our framework with a deep semantic layer, significantly enhancing its capacity to understand complex medical texts. Additionally, by representing the twelve leads as a graph, our framework enables highly accurate disease diagnosis based on spatial and temporal interactions with 12-lead ECG signals. We evaluate the performance of our proposed framework and our framework achieves state-of-the-art performance with accuracy, precision, recall, and F1-score. Code is available at https://github.com/AnonymousBoy123/CVDLLM .
Qiu et al. (Thu,) studied this question.