Acquisition and analysis of real-world sensing data to aid prediction, diagnosis and monitoring of cardiovascular disease

Work on the data collected by software developed by Professor Ciravegna and under release to patients of Prof. Chico. The system will be used by hundreds of patients over the next two years. The system uses a mobile app, a set of bluetooth beacons and a smart watch to monitor patients 24/7 while indoors and outdoors.
Analyse data collected by the existing system and identify signatures of cardiovascular diseases. Define a data analytics platform for doctors to analyse patients' data at an individual and population level. The data analytics platform will support cardiologists in reaching a diagnosis based on the collected, cleaned and modelled data.
Key to the project is development of algorithms that maximise sensing quality while minimising battery power consumption to collect high quality continuous data over long periods; data quality is a major requirement for maximising classification accuracy over long term collection, as well as usability of the collection technology.
The approach will be based on (i) reinforcement learning to optimise sensing strategies and battery impact, (ii) largely unsupervised and semi-supervised machine learning classification algorithms to identify signatures of disease to support diagnosis; transfer learning will be used to create strategies tailored to individual users' behaviour while learning across users and (iii) adaptive data and visualisation analytics methods.