The Semantic Web aims to imbue web resources with rich metadata and meaning so that web content can be consumed by both humans and machines. Linked data is a specific initiative of the Semantic Web metaphor, which advocates the concept of publishing and linking structured data from different sources in order to make the data more useful. The basic motivation behind Linked Data is that the usefulness of data will increase the more it is interlinked with other data.
Social computing is about using computational systems for the study of social behaviours. Social computing has gained growing momentum in the past decade as Web-based social software and media pervades every aspect of our life. Example social software includes those for collective content generation, e.g., Flick and YourTube, and for social networking, e.g., Facebook and Twitter. While different social software has different purposes, they all advocate the same principles, namely, collaboration, interaction, team work, data/information sharing, collective intelligence or problem-solving.
Connected Health is referred to a new model for healthcare delivery that uses technology to provide healthcare remotely. Its central idea is to maximize healthcare resources and provide increased, flexible opportunities for consumers to engage with clinicians and better self-manage their care so that healthcare can be delivered outside of the hospital or doctor’s office, whenever and wherever needed. While connected health has been attempted in a number of practices, e.g., telecare, telehealth, assistive living (such as smart homes) and disease and lifestyle management, existing solutions and technologies are fragmented. Most importantly, there is a lack of a grand vision for the connected health concept.
This Project intends to mashup the linked data concept and social computing metaphor to conceive a novel framework and methodology for connected health that can be used as a blueprint to guide the realisation of the concept. The basic idea is that if we can allow patients, doctors, nurses, healthcare professionals to publish problems, experiences, solutions and opinions in a structured way (e.g., using various ontologies) and to link them to each other. This will gradually evolve/establish a huge knowledge space with valuable, yet implicit knowledge, e.g., a medical condition is linked to a medical solution published by a doctor, and to a rehabilitation practice published by a nurse or medication tips published by a patient(s). This huge structured data/ knowledge in terms of ontologies or some types of formal model can then provide solutions and multiple faceted views for various queries of not only end users but all stakeholders. In addition, advanced features, such as those based on affective information modelling, visualisation, recommendation, etc. can be investigated and developed to enhance connected health delivery. In addition to the framework, the project will develop models, mechanisms, methods, underpinning technologies and prototypes to enable and support the aforementioned idea.
Due to the substantial work required for the proposed idea, this project will focus on a specific research theme. One is, for example, to develop ontologies for modelling interrelationships between various data, the mechanisms for generating linked data, the methods for discovering implicit knowledge and question-answering, and related technologies and a prototype system for testing and evaluation. Other research themes could be affective information modelling and computing, visualisation, recommendation in the context of connected health dependent on the candidate’s interest and background knowledge.
First Supervisor: Chen, L Dr
Second Supervisor: Glass, DH Dr
Third Supervisor: Black, N Prof
Collaboration: This project does not involve collaboration with another establishment