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Clinical prediction models are increasingly used to support health care decisions, helping clinicians estimate disease risk, guide treatment choices, and tailor care to individual patients. At the same time, advances in data science and artificial intelligence have led to a rapid expansion of prediction modelling research. Yet despite this growth, only a small proportion of published models ever reach clinical practice. Improving Health Care with Clinical Prediction Models – From Idea to Impact shows how you can bridge this gap. The book guides readers through the full pathway of prediction modelling in health care: from defining a meaningful clinical problem and identifying existing models to developing, validating, evaluating, and implementing prediction model–based innovations. Combining methodological guidance with real-world clinical examples, the book explains not only how to build robust prediction models but also how to ensure they become useful tools in everyday care. Download the Open Access version at Maastricht University Press - https://doi.org/10.26481/mup.2603

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