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Personalised content exploration engine to support self-management of multiple chronic conditions

Faculty of Engineering and Informatics

About the Project

The self-management model in health aims to empower a person to be more active participant in his or her own care [1]. Involving patients actively in their own care is critical for controlling disease management costs associated with monitoring and assessment. At the same time, encouraging and supporting patients to become more engaged in their own healthcare will simultaneously encourage and support the goals of disease management.

Although there is a vast amount of health-related information, particularly as user-generated content, it is often hard to find reliable information[2]. This PhD project envisages the development of an ambitious personalised content exploration engine that will be able to provide an innovative and engaging experience for patients. The project candidate will explore the use of Semantic Web/Linked Data[3], mHealth [4], and User Generated Content (UGC)[5] research.

The candidate will join a diverse group of researchers engaged in Semantic Web, Digital Health, and Internet of Things (IoT) research streams. The group members already work on world-leading research in this area in the context of funded projects (


1. Bodenheimer, T., Wagner, E. H., & Grumbach, K. (2002). Improving primary care for patients with chronic illness: the chronic care model, Part 2. Jama, 288(15), 1909-1914.

2. Syed-Abdul, S., Fernandez-Luque, L., Jian, W. S., Li, Y. C., Crain, S., Hsu, M. H., ... & Liou, D. M. (2013). Misleading health-related information promoted through video-based social media: anorexia on YouTube. Journal of medical Internet research, 15(2), e30.

3. Bizer, C., Heath, T., & Berners-Lee, T. (2011). Linked data: The story so far. In Semantic services, interoperability and web applications: emerging concepts (pp. 205-227). IGI Global.

4. Kay, M., Santos, J., & Takane, M. (2011). mHealth: New horizons for health through mobile technologies. World Health Organization, 64(7), 66-71.

5. Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook (pp. 73-105). Springer, Boston, MA.

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