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Data science for healthcaremethodolo...

Consoli, Sergio.

Data science for healthcaremethodologies and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science for healthcareedited by Sergio Consoli, Diego Reforgiato Recupero, Milan Petkovic.
Reminder of title:	methodologies and applications /
other author:	Consoli, Sergio.
Published:	Cham :Springer International Publishing :2019.
Description:	xii, 367 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Medical informatics.
Online resource:	https://doi.org/10.1007/978-3-030-05249-2
ISBN:	9783030052492$q(electronic bk.)

Data science for healthcaremethodologies and applications /
Data science for healthcaremethodologies and applications /[electronic resource] :edited by Sergio Consoli, Diego Reforgiato Recupero, Milan Petkovic. - Cham :Springer International Publishing :2019. - xii, 367 p. :ill. (some col.), digital ;24 cm.

Part I: Challenges and Basic Technologies -- Data Science in healthcare: benefits, challenges and opportunities -- Introduction to Classification Algorithms and their Performance Analysis using Medical Examples -- The role of deep learning in improving healthcare -- Part II: Specific Technologies and Applications -- Making effective use of healthcare data using data-to-text technology -- Clinical Natural Language Processing with Deep Learning -- Ontology-based Knowledge Management for Comprehensive Geriatric Assessment and Reminiscence Therapy on Social Robots -- Assistive Robots for the elderly: innovative tools to gather health relevant data -- Overview of data linkage methods for integrating separate health data sources -- A Flexible Knowledge-based Architecture For Supporting The Adoption of Healthy Lifestyles with Persuasive Dialogs -- Visual Analytics for Classifier Construction and Evaluation for Medical Data -- Data Visualization in Clinical Practice -- Using process analytics to improve healthcare processes -- A Multi-Scale Computational Approach to Understanding Cancer Metabolism -- Leveraging healthcare financial analytics for improving the health of entire populations.

This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.

ISBN: 9783030052492$q(electronic bk.)

Standard No.: 10.1007/978-3-030-05249-2doiSubjects--Topical Terms:

196487
Medical informatics.

LC Class. No.: R858

Dewey Class. No.: 610.285

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505 0 $a Part I: Challenges and Basic Technologies -- Data Science in healthcare: benefits, challenges and opportunities -- Introduction to Classification Algorithms and their Performance Analysis using Medical Examples -- The role of deep learning in improving healthcare -- Part II: Specific Technologies and Applications -- Making effective use of healthcare data using data-to-text technology -- Clinical Natural Language Processing with Deep Learning -- Ontology-based Knowledge Management for Comprehensive Geriatric Assessment and Reminiscence Therapy on Social Robots -- Assistive Robots for the elderly: innovative tools to gather health relevant data -- Overview of data linkage methods for integrating separate health data sources -- A Flexible Knowledge-based Architecture For Supporting The Adoption of Healthy Lifestyles with Persuasive Dialogs -- Visual Analytics for Classifier Construction and Evaluation for Medical Data -- Data Visualization in Clinical Practice -- Using process analytics to improve healthcare processes -- A Multi-Scale Computational Approach to Understanding Cancer Metabolism -- Leveraging healthcare financial analytics for improving the health of entire populations.
520 $a This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.
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