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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://doab-dev.siscern.org/handle/20.500.12854/196463
            Keywords
            Predictive analytics; Artificial intelligence; Medicine; Machine learning; Causal discovery; Causal inference; Genomics; Medical knowledge discovery; Clinical risk models; Clinical risk stratification; thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBG Medical equipment and techniques; thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics; thema EDItEUR::M Medicine and Nursing::MQ Nursing and ancillary services; thema EDItEUR::U Computing and Information Technology::UY Computer science; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences; thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine
            DOI
            10.1007/978-3-031-39355-6
            ISBN
            9783031393556, 9783031393549
            Publisher
            Springer Nature
            Publisher website
            http://www.springernature.com/oabooks
            Publication date and place
            Cham, 2024
            Imprint
            Springer International Publishing
            Series
            Health Informatics,
            Pages
            810
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            Credits


            • logo Investir l'avenirInvestir l'avenir
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            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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