Data Science for Healthcare: Methodologies and Applications

✍ Scribed by Sergio Consoli, Diego Reforgiato Recupero, Milan Petković

• Publisher: Springer International Publishing
• Year: 2019
• Tongue: English
• Leaves: 367
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xii
Front Matter ....Pages 1-1
Data Science in Healthcare: Benefits, Challenges and Opportunities (Ziawasch Abedjan, Nozha Boujemaa, Stuart Campbell, Patricia Casla, Supriyo Chatterjea, Sergio Consoli et al.)....Pages 3-38
Introduction to Classification Algorithms and Their Performance Analysis Using Medical Examples (Jan Korst, Verus Pronk, Mauro Barbieri, Sergio Consoli)....Pages 39-73
The Role of Deep Learning in Improving Healthcare (Stefan Thaler, Vlado Menkovski)....Pages 75-116
Front Matter ....Pages 117-117
Making Effective Use of Healthcare Data Using Data-to-Text Technology (Steffen Pauws, Albert Gatt, Emiel Krahmer, Ehud Reiter)....Pages 119-145
Clinical Natural Language Processing with Deep Learning (Sadid A. Hasan, Oladimeji Farri)....Pages 147-171
Ontology-Based Knowledge Management for Comprehensive Geriatric Assessment and Reminiscence Therapy on Social Robots (Luigi Asprino, Aldo Gangemi, Andrea Giovanni Nuzzolese, Valentina Presutti, Diego Reforgiato Recupero, Alessandro Russo)....Pages 173-193
Assistive Robots for the Elderly: Innovative Tools to Gather Health Relevant Data (Alessandra Vitanza, Grazia D’Onofrio, Francesco Ricciardi, Daniele Sancarlo, Antonio Greco, Francesco Giuliani)....Pages 195-215
Overview of Data Linkage Methods for Integrating Separate Health Data Sources (Ana Kostadinovska, Muhammad Asim, Daniel Pletea, Steffen Pauws)....Pages 217-238
A Flexible Knowledge-Based Architecture for Supporting the Adoption of Healthy Lifestyles with Persuasive Dialogs (Mauro Dragoni, Tania Bailoni, Rosa Maimone, Michele Marchesoni, Claudio Eccher)....Pages 239-265
Visual Analytics for Classifier Construction and Evaluation for Medical Data (Jacek Kustra, Alexandru Telea)....Pages 267-287
Data Visualization in Clinical Practice (Monique Hendriks, Charalampos Xanthopoulakis, Pieter Vos, Sergio Consoli, Jacek Kustra)....Pages 289-304
Using Process Analytics to Improve Healthcare Processes (Bart Hompes, Prabhakar Dixit, Joos Buijs)....Pages 305-325
A Multi-Scale Computational Approach to Understanding Cancer Metabolism (Angelo Lucia, Peter A. DiMaggio)....Pages 327-345
Leveraging Financial Analytics for Healthcare Organizations in Value-Based Care Environments (Dieter Van de Craen, Daniele De Massari, Tobias Wirth, Jason Gwizdala, Steffen Pauws)....Pages 347-367

✦ Subjects

Computer Science; Data Mining and Knowledge Discovery; Health Informatics; Health Informatics; Information Storage and Retrieval; Information Systems Applications (incl.Internet)