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    Fundamentals of Clinical Data Science

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    Contributor(s)
    Kubben, Pieter (editor)
    Dumontier, Michel (editor)
    Dekker, Andre (editor)
    Language
    English
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    Abstract
    This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
    URI
    http://library.oapen.org/handle/20.500.12657/22918
    Keywords
    Medicine; Health informatics; Bioinformatics
    DOI
    10.1007/978-3-319-99713-1
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2019
    Classification
    Medical equipment and techniques
    Life sciences: general issues
    Pages
    219
    Rights
    https://creativecommons.org/licenses/by/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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