用医学知识发现患者分组，提升医疗模型泛化能力
Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization
- 基于医学本体构建层级患者分组，自适应调整粒度
- 在四个临床任务上优于8个基线，显著缓解数据分布差异
- 适合需要跨人群泛化的医疗AI研究者
领域泛化在预测性医疗中面临严峻挑战，不同患者群体常表现出数据分布漂移，导致模型性能下降。现有方法在临床场景中表现不佳，主要因缺乏领域标签和未融合临床知识。为此，本文提出UdonCare，一种基于医学本体的层次剪枝方法，通过迭代划分患者为潜在领域，并从患者数据中提取领域不变的（标签）信息。在两个公开数据集上，UdonCare在四个具有显著领域差距的临床预测任务中均优于八个基线，验证了医学知识对提升模型泛化能力的潜力。
原文摘要 · Abstract (English)
Domain generalization has become a critical challenge in predictive healthcare, where different patient groups often exhibit shifting data distributions that degrade model performance. Still, regular domain generalization approaches often struggle in clinical settings due to (1) the absence of domain labels and (2) the lack of clinical insight integration. To address these challenges in healthcare, we aim to explore how medical ontologies can be used to discover dynamic yet hierarchy-grounded patient domains, a partitioning strategy that remains under-explored in prior work. Hence, we introduce UdonCare, a hierarchy-pruning method that iteratively divides patients into latent domains and retrieve domain-invariant (label) information from patient data. On two public datasets, UdonCare shows superiority over eight baselines across four representative clinical prediction tasks with substantial domain gaps, highlighting the potential of medical knowledge for enhancing model generalization.
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