Wang Yubo, Rao Chengfeng, Cheng Qinghua, Yang Jiahao
College of Information Science and Engineering, Northeast University, Shenyang, China.
Front Psychiatry. 2024 Nov 29;15:1418969. doi: 10.3389/fpsyt.2024.1418969. eCollection 2024.
To address the limitations of traditional cardiovascular disease prediction models in capturing dynamic changes and personalized differences in patients, we propose a novel LGAP model based on time-series data analysis. This model integrates Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNN), and Multi-Head Attention mechanisms. By combining patients' time-series data (such as medical records, physical parameters, and activity data) with relationship graph data, the model effectively identifies patient behavior patterns and their interrelationships, thereby improving the accuracy and generalization of cardiovascular disease risk prediction. Experimental results show that LGAP outperforms traditional models on datasets such as PhysioNet and NHANES, particularly in prediction accuracy and personalized health management. The introduction of LGAP offers a new approach to enhancing the precision of cardiovascular disease prediction and the development of customized patient care plans.
为解决传统心血管疾病预测模型在捕捉患者动态变化和个性化差异方面的局限性，我们提出了一种基于时间序列数据分析的新型LGAP模型。该模型集成了长短期记忆（LSTM）网络、图神经网络（GNN）和多头注意力机制。通过将患者的时间序列数据（如病历、身体参数和活动数据）与关系图数据相结合，该模型有效地识别患者行为模式及其相互关系，从而提高心血管疾病风险预测的准确性和泛化能力。实验结果表明，LGAP在PhysioNet和NHANES等数据集上优于传统模型，尤其是在预测准确性和个性化健康管理方面。LGAP的引入为提高心血管疾病预测精度和制定个性化患者护理计划提供了一种新方法。
