将健康社会决定因素融入知识图谱，提升医疗预测公平性
Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare
- 构建含社会因素的医学知识图谱，用异构GCN做药物-疾病链接预测
- 发现社会因素导致预测偏差，重加权边可降低敏感属性影响
- 首次系统评估生物医学图谱中的公平性，适合医疗AI伦理研究者
健康的社会决定因素（SDoH）对患者预后至关重要，但其在生物医学知识图谱中的整合仍不足。本研究基于MIMIC-III数据集与PrimeKG构建了富含SDoH的知识图谱，提出一种新的图嵌入公平性定义，强调对敏感SDoH信息的不变性。采用异构GCN模型进行药物-疾病链接预测，识别出多种SDoH因素带来的预测偏差。为此，提出一种后处理方法，通过战略性重加权与SDoH相关联的边，平衡其对图表示的影响。该工作是首个全面探究含SDoH的生物医学知识图谱公平性问题的研究，不仅凸显了在医疗信息学中纳入SDoH的重要性，还提供了一种有效降低链接预测任务中SDoH偏差的方法，为更公平的医疗推荐铺平道路。代码已公开于：https://github.com/hwq0726/SDoH-KG。
原文摘要 · Abstract (English)
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}.
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