Electronic Resource

Fundamentals of Clinical Data Science

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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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Availability

Detail Information

Series Title

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Call Number

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Publisher

: Springer International Publishing., 2019

Collation

VIII, 219 hlm,: ill, lamp;

Language

English

ISBN/ISSN

9783319997131

Classification

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Content Type

text

Media Type

other

Carrier Type

online resource

Edition

1

Subject(s)

Predictive analytics.
eHealth
mHealth

Specific Detail Info

Provides a resource for healthcare professionals on smart algorithms Integrates the data, modelling, clinical application levels of clinical data science Focuses on relevant non math and code aspects for physicians

Statement of Responsibility

Pieter Kubben, Michel Dumontier, Andre Dekker

Other Information

Cataloger

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Source

https://link.springer.com/book/10.1007/978-3-319-99713-1

Validator

ida

Digital Object Identifier (DOI)

https://doi.org/10.1007/978-3-319-99713-1

Journal Volume

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Journal Issue

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Subtitle

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Parallel Title

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Other version/related

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