Shang Tianqi, He Weiqing, Chen Tianlong, Ding Ying, Wu Huanmei, Zhou Kaixiong, Shen Li
Unversity of Pennsylvania, Philadelphia, PA, USA.
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:481-490. eCollection 2025.
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 https://github.com/hwq0726/SDoH-KG.
健康的社会决定因素（SDoH）在患者健康结果中起着至关重要的作用，然而它们在生物医学知识图谱中的整合仍未得到充分探索。本研究通过使用MIMIC-III数据集和PrimeKG构建一个富含SDoH的知识图谱来解决这一差距。我们为图嵌入引入了一种新颖的公平性公式，重点关注对敏感SDoH信息的不变性。通过采用异质GCN模型进行药物-疾病链接预测，我们检测到与各种SDoH因素相关的偏差。为了减轻这些偏差，我们提出了一种后处理方法该方法对连接到SDoH的边进行策略性地重新加权，平衡它们对图表示的影响。这种方法是对纳入SDoH的生物医学知识图谱中公平性问题的首批全面研究之一。我们的工作不仅强调了在医学信息学中考虑SDoH的重要性，还提供了一种在链接预测任务中减少与SDoH相关偏差的具体方法，为更公平的医疗保健建议铺平了道路。我们的代码可在https://github.com/hwq0726/SDoH-KG获取。
