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

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.

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

• Conference Name: The 6th IEEE International Conference on E-Health and Bioengineering - EHB 2017
• Conference Location: Sinaia, Romania
• Start Date: Jun 22, 2017
• End Date: Jun 24, 2017
• Publication Date: Jul 28, 2017
• Deposit Date: Jun 20, 2019
• 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
• DOI: https://doi.org/10.1109/EHB.2017.7995488
• Keywords: Machine learning algorithms; Decision support system; Asthma self-management
• Public URL: https://hull-repository.worktribe.com/output/2020043
• Publisher URL: https://ieeexplore.ieee.org/document/7995488