Privacy Preserving Data Sharing in Electronic Health Environment. This project aims to improve access to electronic health data (EHD) while still ensuring patient privacy. EHD can provide important information for medical research and health-care resource allocations. However, data sharing in electronic health environments is challenging because of the privacy concerns of customers. Large-scale unauthorised access from internal staff has been reported in Medicare. This project aims to develop ne ....Privacy Preserving Data Sharing in Electronic Health Environment. This project aims to improve access to electronic health data (EHD) while still ensuring patient privacy. EHD can provide important information for medical research and health-care resource allocations. However, data sharing in electronic health environments is challenging because of the privacy concerns of customers. Large-scale unauthorised access from internal staff has been reported in Medicare. This project aims to develop new privacy-preserving algorithms on EHD database federations, which can provide efficient data access yet block inside attacks. It will significantly improve the data available for medical research, while reducing the cost of EHD system management and providing visualised decision supports to medical staff and the government health resource planners.Read moreRead less
Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to ....Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to represent the extracted semantic relationships and novel linkage-analysis based algorithms will be developed for ranking objects. The results from this project will underpin many critical applications such as healthcare.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101610
Funder
Australian Research Council
Funding Amount
$403,398.00
Summary
Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intel ....Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intelligence and will lay the theoretical foundations for building intelligent analysis tools that truly work in tandem with people. The potential benefits to science, society, and the Australian economy, particularly in finance, sensor technologies, and emergency health services would be appreciable.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130101311
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Predicting health status of geriatric patients from user trusted multimedia observations. The information technology developed in this project will provide health care specialists with a better window into the lives of elderly patients. Their behaviour can then be accurately interpreted, potentially leading to earlier recognition of problems and better treatment.