The project topics include the accurate and timely prediction of influenza outbreak in South Australia and Australia, improving cardiac operation outcomes and linking hospital data with external sources to improve mental health outcomes. The outlined studies will have great impact on the health industry including decreasing pressure, understanding the magnitude and location of an outbreak, preventing hospitilisation, reducing readmissions, more efficient patient care at GP’s and cost savings.
Linking Mental Health Data – by linking data from several sources and analysing behavioural patterns over time, mental health readmissions can be predicted and measures can be put in place to reduce them, potentially leading to huge savings for the health sector. This project is being conducted in collaboration with Flinders University
Pandemic Outbreak Modelling – by combining statistical models, several data sources and social media, pandemic outbreaks can be closely monitored and predictions can be made to aid in distributing vaccinations, preparing hospitals and detecting new strains of disease. This project is being conducted in collaboration with the University of Adelaide.
Reducing Complications in Cardiac Procedures – with the application of machine learning to datasets from several cardiac procedures, risk factors can be identified leading to improvements in patient outcomes. This project is being conducted in collaboration with Queen Elizabeth Hospital.
Emergency Waiting Times Application – an application designed to provide useful information in times of an emergency, for example, nearest hospital, current waiting times and other information that will give patients the choice and potentially reduce emergency department burden.