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Springer: Deep Learning in eHealth

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Handbook of Deep Learning Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 136))

Abstract

In recent years, a lot of advances have been made in data analysis using Machine Learning techniques, and specifically using Deep Learners. Deep Learners have been performing particularly well for multimedia mining tasks such as object or face recognition and Natural Language Processing tasks such as speech recognition and voice commands. This opens up a lot of new possibilities for medical applications. Deep Learners can be used for medical imaging, behavioral health analytics, pervasive sensing, translational bioinformatics or predictive diagnosis. This chapter provides an introduction in Deep Learning for health informatics and presents some of its applications for eHealth.

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Author information

Authors and Affiliations

  1. University of Southern Queensland, West Street, Toowoomba, QLD, 4350, Australia

    Peter Wlodarczak

Authors
  1. Peter Wlodarczak
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Correspondence to Peter Wlodarczak .

Editor information

Editors and Affiliations

  1. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  3. University of Canberra, Bruce, ACT, Australia

    Dharmendra Sharma

  4. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

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Wlodarczak, P. (2019). Springer: Deep Learning in eHealth. In: Balas, V., Roy, S., Sharma, D., Samui, P. (eds) Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-030-11479-4_14

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