Data Science for Healthcare: Methodologies and Applications
Sergio Consoli · Diego Reforgiato Recupero · Milan Petković
Feb 2019 · Springer
5.0star
1 reviewreport
Ebook
367
Pages
reportRatings and reviews aren’t verified Learn More
About this ebook
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.
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.
Ratings and reviews
5.0
1 review
About the author
Sergio Consoli 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.
Diego Reforgiato Recupero 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.
Milan Petković 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”.
Diego Reforgiato Recupero 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.
Milan Petković 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”.
Rate this ebook
Tell us what you think.
Reading information
Smartphones and tablets
Install the Google Play Books app for Android and iPad/iPhone. It syncs automatically with your account and allows you to read online or offline wherever you are.
Laptops and computers
You can listen to audiobooks purchased on Google Play using your computer's web browser.
eReaders and other devices
To read on e-ink devices like Kobo eReaders, you'll need to download a file and transfer it to your device. Follow the detailed Help Center instructions to transfer the files to supported eReaders.
