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

Kevin Tsang, Hilary Pinnock, Andrew Wilson, Syed Ahmar Shar

Research output: Contribution to conferencePaperpeer-review

27 Citations (Scopus)
27 Downloads (Pure)


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.
Original languageEnglish
Number of pages5
Publication statusPublished - 27 Aug 2020
Event42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Montreal, Canada
Duration: 20 Jul 202024 Jul 2020


Conference42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society


  • Asthma
  • big data
  • mHealth
  • machine learning
  • self-management

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