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        Artificial Intelligence and Machine Learning in Health Care and Medical Sciences

        Best Practices and Pitfalls

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        Contributor(s)
        Simon, Gyorgy J. (editor)
        Aliferis, Constantin (editor)
        Language
        English
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        Abstract
        This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfallsis a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic.
        URI
        https://library.oapen.org/handle/20.500.12657/88301
        Keywords
        Predictive analytics; Artificial intelligence; Medicine; Machine learning; Causal discovery; Causal inference; Genomics; Medical knowledge discovery; Clinical risk models; Clinical risk stratification
        DOI
        10.1007/978-3-031-39355-6
        ISBN
        9783031393556, 9783031393556, 9783031393549
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Cham, 2024
        Imprint
        Springer International Publishing
        Series
        Health Informatics,
        Classification
        Medical equipment and techniques
        Information technology: general topics
        Nursing and ancillary services
        Computer science
        Biology, life sciences
        Public health and preventive medicine
        Pages
        810
        Rights
        http://creativecommons.org/licenses/by-nc-nd/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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