The early warning paradox

作者：Hugh Logan Ellis, Edward Palmer, James Teo, Martin Whyte, Kenneth Rockwood, Zina Ibrahim · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-024-01408-x · 被引用次数：14 · 研究领域：Machine Learning in Healthcare、Sepsis Diagnosis and Treatment、Explainable Artificial Intelligence (XAI)

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.