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© 2016

Machine Learning for Health Informatics

State-of-the-Art and Future Challenges

  • Andreas Holzinger

Benefits

  • Hot topics in machine learning for health informatics

  • State-of-the-art survey and output of the international HCI-KDD expert network

  • Discusses open problems and future challenges in order to stimulate further research and international progress in this field

Book

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

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, 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

About the editors

HCI-KDD expert network 

The editor Andreas Holzinger is lead of the Holzinger Group, HCI–KDD, Institute for Medical Informatics, Statistics and Documentation at the Medical University Graz, and Associate Professor of Applied Computer Science at the Faculty of Computer Science and Biomedical Engineering at Graz University of Technology. Currently, Andreas is Visiting Professor for Machine Learning in Health Informatics at the Faculty of Informatics at Vienna University of Technology. He serves as consultant for the Canadian, US, UK, Swiss, French, Italian and Dutch governments, for the German Excellence Initiative, and as national expert in the European Commission. His research interests are in supporting human intelligence with machine intelligence to help solve problems in health informatics.
Andreas obtained a PhD in Cognitive Science from Graz University in 1998 and his Habilitation (second PhD) in Computer Science from Graz University of Technology in 2003. Andreas was Visiting Professor in Berlin, Innsbruck, London (twice), and Aachen. He founded the Expert Network HCI–KDD to foster a synergistic combination of methodologies of two areas that offer ideal conditions toward unravelling problems in understanding intelligence: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with machine learning. Andreas is Associate Editor of Knowledge and Information Systems(KAIS), Section Editor of BMC Medical Informatics and Decision Making (MIDM), and member of IFIP WG 12.9 Computational Intelligence, more information: http://hci-kdd.org

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