A collaboration between the School of Health Science and the School of Computer Science and Engineering proposes an AI-driven approach to improve data quality to provide more accurate results in cardiovascular risk-prediction.
The CVMM Theme's funding has enabled Dr Xu and A/Prof Sui to generate pilot data for a large grant application, which will fund the development of a user-friendly tool that allows the public to self-predict their cardiovascular risk based on their dietary intake.
Dr Xiaoyue (Luna) Xu: Scientia Lecturer, School of Population Health
A/Prof. Yulei Sui: Scientia Associate Professor, School of Computer Science and Engineering
Dr Luna is an epidemiologist, working as Scientia Lecturer at the School of Population Health, UNSW. Luna is also an Honorary Fellow at The George Institute for Global Health and the University of Technology Sydney, Australia. Luna serves as an Executive Member of the Cardiac Society of Australia and New Zealand, and Australian Association of Gerontology. Luna’s research focuses on using large data to inform cardiovascular disease prevention and management. Since 2015, Luna has published 75 publications, contributed to 2 book chapters and 6 government reports, including WHO and UN. Luna has delivered 52 conference presentations, attracted >$1.1M in research funding.
A/Prof. Yulei Sui is an ARC Future Fellow and an Associate Professor at School of Computer Science and Engineering, University of New South Wales (UNSW). He is broadly interested in Secure Software Engineering and Machine Learning. In particular, his research focuses on building open-source frameworks for analysis and verification techniques to improve the reliability and security of modern AI systems. He has won several awards in top-tier software engineering conferences, such as ICSE, OOPSLA and received Google ASPIRE Award. He is an IEEE Senior Member and a Fellow of Engineers Australia (FIEAust).
In epidemiology, it is common to use a statistical model to determine diet risks and cardiovascular events, but often yielding conflicting results. This is due to the lack of quality data, mainly due to missing, noisy, inconsistent or unavailable data. New approaches need to be developed to address this problem. So, we proposed an AI-driven approach, aimed to provide accurate results in terms of cardiovascular risk prediction. We also aim to develop user-friendly tool that allows the public to self-predict their cardiovascular risk based on their dietary intake, improving public engagement and awareness on the importance of healthy eating.
Through CVMM funding, we have built the strong collaboration with the School of Computer Science and Engineering and the School of Health Science. This seed funding enables us to generate pilot data for a large grant application, which we are currently preparing to submit to MRFF Cardiovascular Mission grant and the EMCR grant.
I'm really excited about our holiday plans for the end of this year. We are planning to go back to China, spend time with my family, relatives, and friends for a proper vacation. It's been four years since I last visited due to COVID and some personal events, like the birth of my son. This will be his first time in China, and I'm sure he'll be super thrilled, especially to see snow!