Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction
arXiv:2605.24212v1 Announce Type: cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain. For example, high-performing models for out-of-hospital cardiac arrest (OHCA) rely on detailed prehospital measurements routinely collected in high-resource settings but unavailable in many international registries. Existing methods either discard missing covariates, sacrificing predictive information, or rely on untestable assumptions about their target dist
The proliferation of AI models in critical applications, coupled with data fragmentation and varied data collection standards across international healthcare systems, necessitates robust transfer learning methods that address cross-domain challenges like missing covariates.
This development is crucial for expanding the applicability and reliability of AI in high-stakes domains such as healthcare, especially in global contexts where data consistency cannot be assumed, enabling more equitable access to advanced diagnostic and predictive tools.
The ability to deploy advanced clinical prediction models more effectively across diverse healthcare systems, even with structurally missing data, reduces the need for expensive, localized model retraining and improves overall AI utility.
- · Global healthcare providers
- · AI/ML researchers in robust learning
- · Patients in developing regions
- · Medical AI companies
- · Traditional statistical modeling approaches
- · Healthcare systems relying on proprietary, non-transferable data
- · Regions without robust data collection infrastructure
Improved deployment of medical AI in resource-constrained environments.
Accelerated development of generalizable AI solutions across different sectors facing data heterogeneity.
Reduced health disparities globally through more accessible and effective predictive medicine.
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Read at arXiv cs.LG