|Associate Prof Andrew Stranieri|
|Dr Venki Balasubramanian|
|Dr Mehmood Chadhar|
|Ms Sally Firmin|
|Prof John Yearwood||Australia|
|Prof Frada Burstein|
|Dr Isaac Golden||Australia|
|Dr Herbert Jelinek|
|Dr Long Jia|
|Dr Zhaohao Sun|
|Dr Sitalakshmi Venkatraman|
The Health Informatics Laboratory (HIL) aims to apply new information and communication technologies to enhance health and well-being. Using insights research data to make a contribution toward the global health care crisis with research into complementary and alternative medicine informatics, support systems in health, data mining in health and wireless sensor networks for health.
Research directions include:
- Informatics for complementary and alternative medicine.
- Data mining health data: For example, new methods for the rapid assessment of associations between drugs and adverse drug reactions has led to a spin-out company.
- Decision support systems in health: Modelling knowledge to help patients and clinicians make important decisions.
- Tele-health: Research into high-definition, three-dimensional (HD3D) tele-medicine applications.
- TEPPS Technologies to empower people to participate in society.
HIL can provide expertise in the areas of:
- Complementary medicine
- Electronic decision support systems and data mining in health
- Health data and knowledge management
- Health education
- Interoperability of healthcare information systems
HIL seeks to form collaborative partnerships within industry, government bodies and other community organizations. Examples of past and current relationships include:
- Ballarat Health Services
- Charles Sturt University
- Federal Department of Health and Ageing
- General Practice Computing Group
- Grampians Integrated Cancer Service
- Harbin Engineering University (China)
- Pallas Athena Pty Ltd
- Pharmacy Guild of Australia
- Tenon Hospital Paris (France)
- Victorian Department of Human Services
Computational intelligence and decision support
- Business process modelling in health
- Decision Support Based Needs Assessment for Cancer Patients
Data mining and statistics
- Data aggregation over a grid environment
- Longitudinal data analysis
- Mining for ECG defribulation
- Mining for pre-diabetes
- Mining gene regulatory networks from microarray data
- Stream mining patient monitoring data
- Health informatics for an integration of complementary and Western medical systems
- Mathematical model of TCM