Fundamentals Clinical Data Science by Dekker Andre (29 results)

paperback. Condition: Very Good. Fundamentals of Clinical Data Science This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.

paperback. Condition: Very Good. This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.

Condition: New.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: new.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: new.

Condition: As New. Unread book in perfect condition.

Hardcover. Condition: new. Hardcover. 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 books promise is no math, no codeand will explain the topics in a style that is optimized for a healthcare audience. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New. pp. VIII, 219 45 illus., 35 illus. in color.

Condition: New.

Condition: New. In.

Hardcover. Condition: New.

Condition: New. pp. VIII, 219 45 illus., 35 illus. in color. 1 Edition NO-PA16APR2015-KAP.

Hardback. Condition: New. 1st ed. 2019. 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.

Hardback. Condition: New. 1st ed. 2019. 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.

Condition: As New. Unread book in perfect condition.

Condition: New. pp. VIII, 219 45 illus., 35 illus. in color.

Hardcover. Condition: Brand New. 219 pages. 9.25x6.25x0.75 inches. In Stock.

Condition: NEW.

Condition: New. Provides a resource for healthcare professionals on smart algorithms&nbspIntegrates the data, modelling, clinical application levels of clinical data scienceFocuses on relevant non math and c.

Buch. Condition: Neu. Neuware -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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 232 pp. Englisch.

Hardback. Condition: New. 1st ed. 2019. 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.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 Scienceis 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.

Hardcover. Condition: new. Hardcover. 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 books promise is no math, no codeand will explain the topics in a style that is optimized for a healthcare audience. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condition: Very Good. 1. Auflage. Unread, with some shelfwear. Immediately dispatched from Germany.

Hardcover. Condition: New. New. book.

Hardback. Condition: New. 1st ed. 2019. 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.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 Scienceis 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. 232 pp. Englisch.