Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG. We introduce a novel fairness formulation for graph embeddings, focusing on invariance with respect to sensitive SDoH information. Via employing a heterogeneous-GCN model for drug-disease link prediction, we detect biases related to various SDoH factors. To mitigate these biases, we propose a post-processing method that strategically reweights edges connected to SDoHs, balancing their influence on graph representations. This approach represents one of the first comprehensive investigations into fairness issues within biomedical knowledge graphs incorporating SDoH. Our work not only highlights the importance of considering SDoH in medical informatics but also provides a concrete method for reducing SDoH-related biases in link prediction tasks, paving the way for more equitable healthcare recommendations. Our code is available at \url{https://github.com/hwq0726/SDoH-KG}.

翻译：社会健康决定因素（SDoH）对患者健康结果具有关键影响，然而其在生物医学知识图谱中的整合研究仍显不足。本研究通过利用MIMIC-III数据集和PrimeKG构建了一个富含SDoH的知识图谱，以填补这一空白。我们提出了一种新颖的图嵌入公平性框架，重点关注对敏感SDoH信息的不变性。通过采用异质图卷积网络模型进行药物-疾病关联预测，我们检测到与多种SDoH因素相关的偏差。为缓解这些偏差，我们提出一种后处理方法，策略性地对连接SDoH的边进行重加权，以平衡其对图谱表征的影响。该方法是对整合SDoH的生物医学知识图谱中公平性问题的首次系统性探索之一。我们的工作不仅强调了在医学信息学中考虑SDoH的重要性，还为减少关联预测任务中SDoH相关偏差提供了具体方法，为建立更公平的医疗推荐系统奠定了基础。代码发布于\url{https://github.com/hwq0726/SDoH-KG}。