Domain Generalization and Adaptation in Intensive Care with Anchor Regression

• URL: http://arxiv.org/abs/2507.21783v1
• Date: Tue, 29 Jul 2025 13:09:41 GMT
• Title: Domain Generalization and Adaptation in Intensive Care with Anchor Regression
• Authors: Malte Londschien, Manuel Burger, Gunnar Rätsch, Peter Bühlmann,
• Abstract summary: We apply anchor regression and introduce anchor boosting to a large dataset comprising 400,000 patients from nine distinct ICU databases.<n>The methods appear robust to violations of theoretical assumptions, such as anchor exogeneity.<n>We propose a novel conceptual framework to quantify the utility of large external data datasets.
• Score: 10.409435948253845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. The anchor regularization consistently improves 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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