Details
Data Science for Healthcare
Methodologies and Applications|
149,79 € |
|
| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 23.02.2019 |
| ISBN/EAN: | 9783030052492 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<div>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.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div> </div>
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.
<div><b>Sergio Consoli</b> is a Senior Scientist within the Data Science department at Philips Research, Eindhoven, focusing on advancing automated analytical methods used to extract new knowledge from data for health-tech applications. Sergio's education and scientific experience fall in the areas of data science, operations research, artificial intelligence, knowledge engineering, machine learning, and disasters management. He is author of several research publications in peer-reviewed international journals, edited books, and leading conferences in the fields of his work.</div><div><br></div><div><b>Diego Reforgiato Recupero</b> is Associate Professor at the Department of Mathematics and Computer Science of the University of Cagliari, Italy. His interests span from Semantic Web, graph theory and smart grid optimization to sentiment analysis, data mining, big data, machine and deep learning and natural language processing. He is also affiliated within the ISTC institute at the National Research Council (CNR) and co-founder of six ICT companies two of which are university spin-offs. He is author of more than 90 journal, conference papers and book chapters in his research domains.</div><div><br></div><div><b>Milan Petković</b> is the head of the Data Science department in Philips Research which conducts innovation projects for Philips in the domain of data analytics, advanced data management and security. He is also a part-time full professor at the Eindhoven University of Technology. Among his research interests are data science, big data analytics, information security and privacy protection. Milan is also a vice president of the Big Data Value Association, which supports big data public private partnership. He has published more than 50 journal and conference papers as well as several books including a book on “Security, Privacy and Trust in Modern Data Management”.</div>
<div>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.</div><div><br></div><div>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.</div><div><br></div><div>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.</div>
Connects machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies to healthcare applications. Highlights the successful application of these technologies in various healthcare areas. Intended for data scientists involved in the healthcare or medical sector.
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