用图神经网络解决成瘾患者住院分配的极端类别不平衡问题
GRACE: Graph Neural Networks for Locus-of-Care Prediction under Extreme Class Imbalance
- 构建图神经网络框架，将护理定位预测建模为结构化学习问题
- 在真实数据上使少数类F1提升11%-35%，联合微调再增15.8%
- 适合医疗资源分配、临床决策支持系统研究者参考
确定成瘾患者的适当护理场所是影响治疗效果和资源利用的关键临床决策。由于专业治疗资源（如住院床位或医护人员）不足，亟需自动化框架支持。当前方法在成瘾数据集中面临严重的类别不平衡问题。为此，我们提出一种新型图神经网络框架GRACE，将护理定位预测形式化为结构化学习任务，并设计新方法生成无偏元图以训练GNN，缓解类别不平衡。基于真实世界数据的实验表明，相比现有基线，少数类F1分数提升11%-35%；若联合微调输入的基底嵌入与GRACE其余组件，则性能显著提升15.8%。
原文摘要 · Abstract (English)
Determining the appropriate locus of care for addiction patients is one of the most critical clinical decisions that affects patient treatment outcomes and effective use of resources. With a lack of sufficient specialized treatment resources, such as inpatient beds or staff, there is an unmet need to develop an automated framework for the same. Current decision-making approaches suffer from severe class imbalances in addiction datasets. To address this limitation, we propose a novel graph neural network (GRACE) framework that formalizes locus of care prediction as a structured learning problem. In addition, we propose a new approach of obtaining an unbiased meta-graph to train a GNN to overcome the class imbalance problem. Experimental results with real-world data show an improvement of 11-35% in terms of the F1 score of the minority class over competitive baselines. Further, if we jointly finetune the base embedding fed into GRACE as input together with the rest of the GNN component of GRACE, there is a remarkable boost of 15.8% in performance.
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