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Fundamentals of clinical data science

Dekker, Andre.

Fundamentals of clinical data science

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fundamentals of clinical data scienceedited by Pieter Kubben, Michel Dumontier, Andre Dekker.
其他作者:	Kubben, Pieter.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	viii, 219 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Medical informatics.
電子資源:	https://doi.org/10.1007/978-3-319-99713-1
ISBN:	9783319997131$q(electronic bk.)

Fundamentals of clinical data science
Fundamentals of clinical data science[electronic resource] /edited by Pieter Kubben, Michel Dumontier, Andre Dekker. - Cham :Springer International Publishing :2019. - viii, 219 p. :ill., digital ;24 cm.

Data sources -- Data at scale -- Standards in healthcare data -- Using FAIR data / data stewardship -- Privacy / deidentification -- Preparing your data -- Creating a predictive model -- Diving deeper into models -- Validation and Evaluation of reported models -- Clinical decision support systems -- Mobile app development -- Operational excellence -- Value Based Healthcare (Regulatory concerns)

Open access.

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.

ISBN: 9783319997131$q(electronic bk.)

Standard No.: 10.1007/978-3-319-99713-1doiSubjects--Topical Terms:

196487
Medical informatics.

LC Class. No.: R858 / .F863 2019

Dewey Class. No.: 610.285

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506 $a Open access.
520 $a 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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