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oapen-20.500.12657-229182024-03-22T19:23:34Z Fundamentals of Clinical Data Science Kubben, Pieter Dumontier, Michel Dekker, Andre Medicine Health informatics Bioinformatics thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBG Medical equipment and techniques thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues 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. 2020-03-18 13:36:15 2020-04-01T08:56:21Z 2020-04-01T08:56:21Z 2019 book 1007243 http://library.oapen.org/handle/20.500.12657/22918 eng application/pdf n/a 1007243.pdf https://www.springer.com/9783319997131 Springer Nature 10.1007/978-3-319-99713-1 10.1007/978-3-319-99713-1 6c6992af-b843-4f46-859c-f6e9998e40d5 219 Cham open access
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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.
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