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Studies in Computational Intelligence

Explainable AI in Healthcare and Medicine

Building a Culture of Transparency and Accountability

Editors: Shaban-Nejad, Arash, Michalowski, Martin, Buckeridge, David L. (Eds.)

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  • Highlights the latest advances in explainable AI in health care and medicine by presenting significant findings on theory, methods, systems, and applications Includes revised versions of selected papers presented at the 2020 AAAI International Workshop on Health Intelligence (W3PHIAI2020), held in New York City, USA, on February 7, 2020 Interconnects three major fields: artificial intelligence, medicine, and clinical and public health informatics
  • Emphasizes potential and current applications, clinical and public health benefits, and industrial/entrepreneurial opportunities
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eBook 117,69 €
price for India (gross)
  • ISBN 978-3-030-53352-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 139,99 €
price for India (gross)
  • ISBN 978-3-030-53351-9
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
About this book

This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence.

Table of contents (32 chapters)

Table of contents (32 chapters)
  • Explainability and Interpretability: Keys to Deep Medicine

    Pages 1-10

    Shaban-Nejad, Arash (et al.)

  • Fast Similar Patient Retrieval from Large Scale Healthcare Data: A Deep Learning-Based Binary Hashing Approach

    Pages 11-21

    Wang, Ke (et al.)

  • A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

    Pages 23-36

    Mikalsen, Karl Øyvind (et al.)

  • Machine Learning Discrimination of Parkinson’s Disease Stages from Walker-Mounted Sensors Data

    Pages 37-44

    Seedat, Nabeel (et al.)

  • Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Reinforcement Learning

    Pages 45-53

    Zhu, Taiyu (et al.)

Buy this book

eBook 117,69 €
price for India (gross)
  • ISBN 978-3-030-53352-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 139,99 €
price for India (gross)
  • ISBN 978-3-030-53351-9
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Explainable AI in Healthcare and Medicine
Book Subtitle
Building a Culture of Transparency and Accountability
Editors
  • Arash Shaban-Nejad
  • Martin Michalowski
  • David L. Buckeridge
Series Title
Studies in Computational Intelligence
Series Volume
914
Copyright
2021
Publisher
Springer International Publishing
Copyright Holder
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-53352-6
DOI
10.1007/978-3-030-53352-6
Hardcover ISBN
978-3-030-53351-9
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XXII, 344
Number of Illustrations
26 b/w illustrations, 84 illustrations in colour
Topics