Assessing machine learning algorithms for self-management of asthma

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

doi:10.1109/EHB.2017.7995488

Assessing machine learning algorithms for self-management of asthma

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

The University of Manchester

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	2017 E-Health and Bioengineering Conference, EHB 2017
Publisher	IEEE
Pages	571-574
Number of pages	4
ISBN (Electronic)	9781538603581
DOIs	https://doi.org/10.1109/EHB.2017.7995488
Publication status	Published - 31 Jul 2017
Event	6th IEEE International Conference on E-Health and Bioengineering, EHB 2017 - Sinaia, Romania Duration: 22 Jun 2017 → 24 Jun 2017

Conference

Conference	6th IEEE International Conference on E-Health and Bioengineering, EHB 2017
Country/Territory	Romania
City	Sinaia
Period	22/06/17 → 24/06/17

Keywords

asthma self-management
decision support system
machine learning algorithms

Research Beacons, Institutes and Platforms

Manchester Institute of Biotechnology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/EHB.2017.7995488

Cite this

@inproceedings{2414dfbf10f84bcf8bc7860db7336c80,

title = "Assessing machine learning algorithms for self-management of asthma",

abstract = "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.",

keywords = "asthma self-management, decision support system, machine learning algorithms",

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

year = "2017",

month = jul,

day = "31",

doi = "10.1109/EHB.2017.7995488",

language = "English",

pages = "571--574",

booktitle = "2017 E-Health and Bioengineering Conference, EHB 2017",

publisher = "IEEE",

address = "United States",

note = "6th IEEE International Conference on E-Health and Bioengineering, EHB 2017 ; Conference date: 22-06-2017 Through 24-06-2017",

}

Kocsis, O, Arvanitis, G, Lalos, A, Moustakas, K, Sont, JK, Honkoop, PJ, Chung, KF, Bonini, M, Usmani, OS, Fowler, S & Simpson, A 2017, Assessing machine learning algorithms for self-management of asthma. in 2017 E-Health and Bioengineering Conference, EHB 2017., 7995488, IEEE, pp. 571-574, 6th IEEE International Conference on E-Health and Bioengineering, EHB 2017, Sinaia, Romania, 22/06/17. https://doi.org/10.1109/EHB.2017.7995488

TY - GEN

T1 - Assessing machine learning algorithms for self-management of asthma

AU - Kocsis, Otilia

AU - Arvanitis, Gerasimos

AU - Lalos, Aris

AU - Moustakas, Konstantinos

AU - Sont, Jacob K.

AU - Honkoop, Persijn J.

AU - Chung, Kian Fan

AU - Bonini, Matteo

AU - Usmani, Omar S.

AU - Fowler, Stephen

AU - Simpson, Andrew

PY - 2017/7/31

Y1 - 2017/7/31

N2 - 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.

AB - 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.

KW - asthma self-management

KW - decision support system

KW - machine learning algorithms

UR - http://www.scopus.com/inward/record.url?scp=85028526935&partnerID=8YFLogxK

U2 - 10.1109/EHB.2017.7995488

DO - 10.1109/EHB.2017.7995488

M3 - Conference contribution

AN - SCOPUS:85028526935

SP - 571

EP - 574

BT - 2017 E-Health and Bioengineering Conference, EHB 2017

PB - IEEE

T2 - 6th IEEE International Conference on E-Health and Bioengineering, EHB 2017

Y2 - 22 June 2017 through 24 June 2017

ER -

Assessing machine learning algorithms for self-management of asthma

Abstract

Conference

Keywords

Research Beacons, Institutes and Platforms

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this