Application of machine learning algorithms for asthma management with mHealth: A clinical review

Tsang, Kevin C. H., Pinnock, Hilary, Wilson, Andrew M. and Shah, Syed Ahmar (2022) Application of machine learning algorithms for asthma management with mHealth: A clinical review. Journal of Asthma and Allergy, 15. pp. 855-873. ISSN 1178-6965

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Background: Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods: We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results: Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion: In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

Item Type: Article
Additional Information: Funding Information: This work is funded by Asthma+Lung UK as part of the Asthma UK Centre for Applied Research [AUK-AC-2018-01] Publisher Copyright: © 2022 Tsang et al.
Uncontrolled Keywords: artificial intelligence,chronic disease,remote monitoring; asthma,self-management,smart devices,immunology and allergy,pulmonary and respiratory medicine,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2700/2723
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups: Faculty of Medicine and Health Sciences > Research Centres > Population Health
Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
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Depositing User: LivePure Connector
Date Deposited: 29 Aug 2023 13:31
Last Modified: 13 Nov 2023 17:58
DOI: 10.2147/JAA.S285742


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