Asthma is affecting around 5.4 million people in the UK. Currently, there is no cure for asthma. However existing treatments, such as inhalers, can be used to manage the condition better. The “optimal” self-management strategy should include a personalised asthma action plan supported by regular professional review and self-management education.
Mobile-health applications (mHealth) have come to the forefront of self-management due to smartphones becoming ubiquitous. Where each device is packed with sensors, connected to the internet, and portable, which allows for non-intrusive monitoring. mHealth can play a role in promoting adherence to the self-management strategy by simplifying and reducing the number of recurring active input required by the patient; while providing passive monitoring throughout the day and appropriately timed alerts.
The research aims to create a system for a personalised early predictor of asthma exacerbation, consisting of a mobile app, a server and the algorithm. We aim to develop an ML-based algorithm that is capable of learning as more data is collected, in addition to building upon existing research in using low-cost medical devices and wearable devices. This system will allow better self-management by preventing episodes through spotting early warning signs.
My research interests are machine learning, prediction models and mobile technologies.
K. C. H. Tsang, H. Pinnock, A. M. Wilson and S. A. Shah, “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, Montreal, 2020.