The early warning paradox - Hugh Logan Ellis; Edward Palmer; James T. Teo; Martin Whyte; Kenneth Rockwood; Zina Ibrahim (npj Digital Medicine)
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.