The early warning paradox.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 39900787.
- Also identified by DOI 10.1038/s41746-024-01408-x and PMC identifier 11790821.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.