Machine Learning for Health Informatics

State-of-the-Art and Future Challenges

  • Andreas Holzinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9605)

Table of contents

  1. Front Matter
    Pages I-XXII
  2. Andreas Holzinger
    Pages 1-24
  3. Olcay Taner Yıldız, Ozan İrsoy, Ethem Alpaydın
    Pages 25-36
  4. Vincenzo Manca
    Pages 37-58
  5. Matic Perovšek, Matjaž Juršič, Bojan Cestnik, Nada Lavrač
    Pages 59-98
  6. Joao H. Bettencourt-Silva, Gurdeep S. Mannu, Beatriz de la Iglesia
    Pages 99-124
  7. Mohammad Havaei, Nicolas Guizard, Hugo Larochelle, Pierre-Marc Jodoin
    Pages 125-148
  8. Jefferson Tales Oliva, João Luís Garcia Rosa
    Pages 149-160
  9. Akara Supratak, Chao Wu, Hao Dong, Kai Sun, Yike Guo
    Pages 161-182
  10. Dragana Miljkovic, Darko Aleksovski, Vid Podpečan, Nada Lavrač, Bernd Malle, Andreas Holzinger
    Pages 209-220
  11. Ernestina Menasalvas, Consuelo Gonzalo-Martin
    Pages 221-242
  12. Sebastian J. Teran Hidalgo, Michael T. Lawson, Daniel J. Luckett, Monica Chaudhari, Jingxiang Chen, Arkopal Choudhury et al.
    Pages 259-288
  13. Satya S. Sahoo, Annan Wei, Curtis Tatsuoka, Kaushik Ghosh, Samden D. Lhatoo
    Pages 303-318
  14. Chloé-Agathe Azencott
    Pages 319-336
  15. Aryya Gangopadhyay, Rose Yesha, Eliot Siegel
    Pages 337-356
  16. Sebastian Robert, Sebastian Büttner, Carsten Röcker, Andreas Holzinger
    Pages 357-376
  17. Clayton R. Pereira, Danillo R. Pereira, Joao P. Papa, Gustavo H. Rosa, Xin-She Yang
    Pages 377-390

About this book

Introduction

Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization.
Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence.
This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

Keywords

algorithms artificial intelligence big data classification data mining data science decision support systems deep learning health informatics Human-Computer Interaction (HCI) image processing Knowledge Discovery in Databases (KDD) knowledge-based systems machine learning Natural Language Processing (NLP) neural networks semantics text mining visualization

Editors and affiliations

  • Andreas Holzinger
    • 1
  1. 1.Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-50478-0
  • Copyright Information Springer International Publishing AG 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-50477-3
  • Online ISBN 978-3-319-50478-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book

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