Advanced Data Analytics in Health

  • Philippe J. Giabbanelli
  • Vijay K. Mago
  • Elpiniki I. Papageorgiou

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 93)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Data Exploration and Visualization

    1. Front Matter
      Pages 1-1
    2. Philippe J. Giabbanelli, Magda Baniukiewicz
      Pages 21-40
  3. Modeling and Simulation

    1. Front Matter
      Pages 41-41
    2. Amin Khademi, Donglan Zhang, Philippe J. Giabbanelli, Shirley Timmons, Chengqian Luo, Lu Shi
      Pages 43-58
    3. Noshad Rahimi, Antonie J. Jetter, Charles M. Weber, Katherine Wild
      Pages 59-74
  4. Machine Learning

    1. Front Matter
      Pages 75-75
    2. Abdollah Amirkhani, Mojtaba Kolahdoozi, Elpiniki I. Papageorgiou, Mohammad R. Mosavi
      Pages 99-116
    3. Lauren E. Charles, William Smith, Jeremiah Rounds, Joshua Mendoza
      Pages 117-131
  5. Case Studies

    1. Front Matter
      Pages 133-133
    2. Teresa B. Gibson, Zeynal Karaca, Gary Pickens, Michael Dworsky, Eli Cutler, Brian J. Moore et al.
      Pages 135-149
    3. Teresa B. Gibson, J. Ross Maclean, Ginger S. Carls, Emily D. Ehrlich, Brian J. Moore, Colin Baigel
      Pages 151-162
  6. Challenges and New Frontiers

    1. Front Matter
      Pages 183-183
    2. Kevin Bouchard, Jianguo Hao, Bruno Bouchard, Sébastien Gaboury, Mohammed Tarik Moutacalli, Charles Gouin-Vallerand et al.
      Pages 185-200
    3. Shamith A. Samarajiwa, Ioana Olan, Dóra Bihary
      Pages 201-216

About this book


This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. The challenges of working with big data are explored throughout the book, while the benefits are also emphasized through the discoveries made possible by linking large datasets. Methods include thorough case studies from statistics, as well as the newest facets of data analytics: data visualization, modeling and simulation, and machine learning. The diversity of datasets is illustrated through chapters on networked data, image processing, and text, in addition to typical structured numerical datasets. While the methods, types of data, and scale have been individually covered elsewhere, by bringing them all together under one “umbrella” the book highlights synergies, while also helping scholars fluidly switch between tools as needed. New challenges and emerging frontiers are also discussed, helping scholars grasp how methods will need to change in response to the latest challenges in health.


Data Science Healthcare Systems Decision Support Systems Health Informatics Data Analytics

Editors and affiliations

  • Philippe J. Giabbanelli
    • 1
  • Vijay K. Mago
    • 2
  • Elpiniki I. Papageorgiou
    • 3
  1. 1.Computer Science DepartmentFurman UniversityGreenvilleUSA
  2. 2.Department of Computer ScienceLakehead UniversityThunder BayCanada
  3. 3.Department of Computer EngineeringTechnological Educational InstituteLamiaGreece

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-77910-2
  • Online ISBN 978-3-319-77911-9
  • Series Print ISSN 2190-3018
  • Series Online ISSN 2190-3026
  • Buy this book on publisher's site
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