Titlebook: AI and Big Data in Cardiology; A Practical Guide Nicolas Duchateau,Andrew P. King Textbook 2023 The Editor(s) (if applicable) and The Autho

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Hyperfunctions Depending on Parameters,right problem is emphasised. A review is provided of different types of machine learning model, and pointers are provided about how to design and train a model to meet the requirements of the chosen problem. Important considerations regarding validating the trained model are also discussed. A review

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Upper (Lower)-Type Hyperfunctions, can be defined based on the equation for a straight line. A more general scheme for optimization of the parameters of classifiers is introduced, based on gradient descent and its variants. We then see how the basic classifier model can be extended to produce simple artificial neural networks such a

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Poisson-Schwarz Integral Formulae,predicting response to treatment. A clinical opinion piece summarises the role of prognosis in clinical care and highlights the areas where AI has already had an impact in this area. The technical section summarizes the state-of-the-art in outcome prediction, focusing on three clinical applications

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Preparing the Subject for Inductionort clinical decision making. This chapter deals with the requirements, achievements and challenges of AI for decision support in cardiology. The chapter first examines the way in which medical practitioners make decisions, and based upon this discusses how AI can assist in this process. A key chara

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Applied Hypnosis and Hyperempirialligence (AI) in cardiology. We attempt to look into the future to see what AI can do in cardiology, and how it might interact with other fields to maximise patient benefit. We hope to strike an optimistic note, but with the proviso that the undoubted potential benefits of AI in cardiology will not