The early warning paradox
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Authors
Logan Ellis, Hugh
Palmer, Edward
Teo, James T.
Whyte, Martin
Rockwood, Kenneth
Ibrahim, Zina
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Issue Date
2025
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Article
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Abstract
Machine learning models in healthcare aim to predict critical outcomes but often overlook existing Early Warning Systems’ impact. Using data from King’s College Hospital, we demonstrate how current evaluation methods can lead to paradoxical results. We discuss challenges in developing ML models from retrospective data and propose a novel approach focused on identifying when patients enter a ‘risk state’ through latent health representations, potentially transforming clinical decision-making.
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Journal
npj Digital Medicine
Volume
8
Issue
1