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Fundamentals of clinical data science
~
Kubben, Pieter.
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Fundamentals of clinical data science
Record Type:
Electronic resources : Monograph/item
Title/Author:
Fundamentals of clinical data science/ edited by Pieter Kubben, Michel Dumontier, Andre Dekker.
other author:
Kubben, Pieter.
Published:
Cham :Springer International Publishing : : 2019.,
Description:
viii, 219 p. :ill., digital ;24 cm.
[NT 15003449]:
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)
Contained By:
Springer eBooks
Subject:
Medical informatics. -
Online resource:
https://doi.org/10.1007/978-3-319-99713-1
ISBN:
9783319997131
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
Standard No.: 10.1007/978-3-319-99713-1doiSubjects--Topical Terms:
661258
Medical informatics.
LC Class. No.: R858 / .F863 2019
Dewey Class. No.: 610.285
Fundamentals of clinical data science
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edited by Pieter Kubben, Michel Dumontier, Andre Dekker.
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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)
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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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Medicine (Springer-11650)
based on 0 review(s)
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ALL
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Items
1 records • Pages 1 •
1
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Attachments
W9368871
電子資源
11.線上閱覽_V
電子書
EB R858 .F863 2019
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1 records • Pages 1 •
1
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