构建疾病关联图谱，提升高血压并发症风险预测精度
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction
- 融合患者与疾病差异网络，构建双层图结构捕捉共病关系
- 在糖尿病和冠心病风险预测上准确率优于现有模型
- 揭示疾病进展路径，有助于理解糖尿病与冠心病发病机制
高血压共病给患者和社会带来沉重负担，早期识别对及时干预至关重要，但仍是难题。本研究提出一种联合图表示学习框架（CGRL），通过疾病编码构建患者网络与疾病差异网络，基于基础差异网络生成三种共病网络特征，以捕捉共病与风险疾病间的潜在关联；结合计算结构干预与特征表示学习，实现对糖尿病和冠心病风险的预测；并通过共病模式分析，探索疾病进展路径，可能揭示糖尿病与冠心病的病理发生机制。结果表明，基于差异网络提取的特征具有重要意义，所提框架在准确性上优于其他强模型。
原文摘要 · Abstract (English)
The comorbidities of hypertension impose a heavy burden on patients and society. Early identification is necessary to prompt intervention, but it remains a challenging task. This study aims to address this challenge by combining joint graph learning with network analysis. Motivated by this discovery, we develop a Conjoint Graph Representation Learning (CGRL) framework that: a) constructs two networks based on disease coding, including the patient network and the disease difference network. Three comorbidity network features were generated based on the basic difference network to capture the potential relationship between comorbidities and risk diseases; b) incorporates computational structure intervention and learning feature representation, CGRL was developed to predict the risks of diabetes and coronary heart disease in patients; and c) analysis the comorbidity patterns and exploring the pathways of disease progression, the pathological pathogenesis of diabetes and coronary heart disease may be revealed. The results show that the network features extracted based on the difference network are important, and the framework we proposed provides more accurate predictions than other strong models in terms of accuracy.
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