Application of machine learning to support self-management of asthma with mHealth

Tsang, Kevin, Pinnock, Hilary, Wilson, Andrew and Shar, Syed Ahmar (2020) Application of machine learning to support self-management of asthma with mHealth. In: 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2020-07-20 - 2020-07-24, Canada.

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Abstract

While there have been several efforts to use mHealth technologies to support asthma management, none so far offer personalised algorithms that can provide real-time feedback and tailored advice to patients based on their monitoring. This work employed a publicly available mHealth dataset, the Asthma Mobile Health Study (AMHS), and applied machine learning techniques to develop early warning algorithms to enhance asthma self-management. The AMHS consisted of longitudinal data from 5,875 patients, including 13,614 weekly surveys and 75,795 daily surveys. We applied several well-known supervised learning algorithms (classification) to differentiate stable and unstable periods and found that both logistic regression and naïve Bayes-based classifiers provided high accuracy (AUC > 0.87). We found features related to the use of quick-relief puffs, night symptoms, frequency of data entry, and day symptoms (in descending order of importance) as the most useful features to detect early evidence of loss of control. We found no additional value of using peak flow readings to improve population level early warning algorithms.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: asthma,big data,mhealth,machine learning,self-management,signal processing,biomedical engineering,computer vision and pattern recognition,health informatics ,/dk/atira/pure/subjectarea/asjc/1700/1711
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: LivePure Connector
Date Deposited: 16 Apr 2020 00:24
Last Modified: 20 Oct 2020 00:04
URI: https://ueaeprints.uea.ac.uk/id/eprint/74752
DOI: 10.1109/EMBC44109.2020.9175679

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