用锚回归提升重症监护模型跨医院泛化能力
Domain Generalization and Adaptation in Intensive Care with Anchor Regression
- 基于锚回归与树形扩展方法，建模多中心重症数据分布差异
- 在9个数据库40万患者上，外推性能显著提升，尤其对差异大的医院
- 提出三阶段框架，指导外部数据使用策略，适合临床部署研究者
临床预测模型在新医院部署时常因分布偏移而性能下降。本文在包含9个不同重症监护室（ICU）数据库、共40万患者的大型数据集上，开展因果启发的域泛化大规模研究。采用锚回归并引入一种新型树基非线性扩展——锚增强，结果表明锚正则化显著提升了跨域性能，尤其在目标域差异最大时效果更明显。该方法对理论假设（如锚外生性）的违反也表现出鲁棒性。此外，提出一个新概念框架，通过评估目标域可用数据量下的性能表现，识别出三种情形：(i) 域泛化阶段，仅使用外部模型即可；(ii) 域适应阶段，微调外部模型最优；(iii) 数据充裕阶段，外部数据不再带来增益。
原文摘要 · Abstract (English)
The performance of predictive models in clinical settings often degrades when deployed in new hospitals due to distribution shifts. This paper presents a large-scale study of causality-inspired domain generalization on heterogeneous multi-center intensive care unit (ICU) data. We apply anchor regression and introduce anchor boosting, a novel, tree-based nonlinear extension, to a large dataset comprising 400,000 patients from nine distinct ICU databases. We find that anchor regularization yields improvements of out-of-distribution performance, particularly for the most dissimilar target domains. The methods appear robust to violations of theoretical assumptions, such as anchor exogeneity. Furthermore, we propose a novel conceptual framework to quantify the utility of large external data datasets. By evaluating performance as a function of available target-domain data, we identify three regimes: (i) a domain generalization regime, where only the external model should be used, (ii) a domain adaptation regime, where refitting the external model is optimal, and (iii) a data-rich regime, where external data provides no additional value.
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