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Assessing machine learning algorithms for self-management of asthma

Kocsis, Otilia; Arvanitis, Gerasimos; Lalos, Aris; Moustakas, Konstantinos; Sont, Jacob K.; Honkoop, Persijn J.; Chung, Kian Fan; Bonini, Matteo; Usmani, Omar S.; Fowler, Stephen; Simpson, Andrew

Authors

Otilia Kocsis

Gerasimos Arvanitis

Aris Lalos

Konstantinos Moustakas

Jacob K. Sont

Persijn J. Honkoop

Kian Fan Chung

Matteo Bonini

Omar S. Usmani

Stephen Fowler



Abstract

© 2017 IEEE. Control and monitoring of asthma progress is highly important for patient's quality of life and healthcare management. Emerging tools for self-management of various chronic diseases have the potential to support personalized patient guidance. This work presents the design aspects of the myAirCoach decision support system, with focus on the assessment of three machine learning approaches as support tools the first prototype implementation.

Start Date Jun 22, 2017
Publication Date Jul 28, 2017
Publisher Institute of Electrical and Electronics Engineers
Pages 571-574
Book Title 2017 E-Health and Bioengineering Conference (EHB)
ISBN 978-1-5386-0358-1
APA6 Citation Kocsis, O., Arvanitis, G., Lalos, A., Moustakas, K., Sont, J. K., Honkoop, P. J., …Simpson, A. (2017). Assessing machine learning algorithms for self-management of asthma. In 2017 E-Health and Bioengineering Conference (EHB), 571-574. https://doi.org/10.1109/EHB.2017.7995488
DOI https://doi.org/10.1109/EHB.2017.7995488
Keywords Machine learning algorithms; Decision support system; Asthma self-management
Publisher URL https://ieeexplore.ieee.org/document/7995488
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