Due to the insufficient diagnosis and treatment capabilities at the grassroots level, there are still deficiencies in the early identification and early warning of acute exacerbation of Chronic obstructive pulmonary disease (COPD), often resulting in a high prevalence rate and high burden, but the screening rate is relatively low. In order to gradually improve this situation. In this paper, this study develop a Causal Heterogeneous Graph Representation Learning (CHGRL) method for COPD comorbidity risk prediction method that: a) constructing a heterogeneous Our dataset includes the interaction between patients and diseases; b) A cause-aware heterogeneous graph learning architecture has been constructed, combining causal inference mechanisms with heterogeneous graph learning, which can support heterogeneous graph causal learning for different types of relationships; and c) Incorporate the causal loss function in the model design, and add counterfactual reasoning learning loss and causal regularization loss on the basis of the cross-entropy classification loss. We evaluate our method and compare its performance with strong GNN baselines. Following experimental evaluation, the proposed model demonstrates high detection accuracy.

翻译：由于基层诊疗能力不足，慢性阻塞性肺疾病（COPD）急性加重的早期识别与预警仍存在不足，常导致高患病率与高负担，但筛查率相对较低。为逐步改善这一现状，本研究提出一种用于COPD共病风险预测的因果异质图表征学习（CHGRL）方法，该方法具备以下特点：a）构建包含患者与疾病间交互关系的异质图数据集；b）设计一种因果感知的异质图学习架构，将因果推断机制与异质图学习相结合，可支持针对不同类型关系的异质图因果学习；c）在模型设计中引入因果损失函数，在交叉熵分类损失基础上增加反事实推理学习损失与因果正则化损失。我们评估了所提方法，并与多种强基准图神经网络模型进行了性能对比。实验结果表明，该模型具有较高的检测准确率。