AUT has secured more than $2.1m to develop new data science technology for mental health diagnosis in MBIE Catalyst Programme.
The project is part of the New Zealand-Singapore Data Science Research Programme.
Aotearoa New Zealand has one of the highest prevalence rates of depression worldwide. It accounts for half of the annual suicides and attempted suicides, particularly among 13-25 years old. There is a need to develop new data science methods for accurate diagnosis/prognosis of mental illness and suggest optimal interventions.
The research focuses on developing a new computational neuro-genetic modelling based on machine-learning/AI methods for diagnosis of mental health issues, led by AUT's School of Engineering, Computer and Mathematical Sciences (ECMS). This study boasts an unprecedented level of data varieties, including cutting edge genomics, proteomics and metabonomic technologies.
This project is jointly coordinated via AUT's ECMS and our Singapore research partner from the School of Biological Sciences, Nanyang Technological University via the Catalyst: Strategic – New Zealand-Singapore Data Science Research Programme, supported by the New Zealand Ministry of Business Innovation and Employment and Singapore Data Science Consortium.
The main outcomes:
In this project, the initial computational modelling will begin on six years' worth of data collected in Singapore, of 600 young people, some of them manifesting health disorders. The data follows for two years and measures a large number of variables, including cognitive, biomedical, psychological, behavioural, genomic and proteomic. Using AUT's patented NeuCube and personalised modelling methods as a starting point, Professor Kasabov says that at the end of the research period, they hope to have a pan-omics AI platform that can take a various and large amount of data from young people over time to indicate early who is at risk of developing a mental health problem, such as depression and other disorders, which is an acute problem in Singapore affecting about 18% of young people due to fast pace of life, and can be life-limiting, debilitating and even life-threatening, costing also a lot to the society.
"Mental illness, depression and depression-linked suicide are huge problems in both Aotearoa New Zealand and Singapore. Late diagnosis is the thing we can avoid with intelligent predictive computational models. The hospital and the cemetery are full of people who could have been helped earlier. We are hoping this project results in a platform that can help diagnose those at risk early and recommend personalised treatment and prevention plans," concludes Professor Kasabov.
The project will be led by Dr Maryam Doborjeh, a young PhD graduate and now a lecturer at AUT's ECMS. Professor Nikola Kasabov, who was her PhD supervisor, is the Science Leader. Professor Kasabov says having young researchers in leading roles helps build a new generation of researchers who will lead the field of data science in the future.
It will also involve Prof. Edmund Lai from AUT's ECMS, Dr Zohreh Doborjeh (University of Auckland), Dr Margaret Hinepo Williams (Public and Māori Health Research Lead at AUT), and Prof. Alex Sumich (Nottingham Trent University) as a consultant.
Our Singapore partner has also secured matched funding supported by SDSC, led by Dr Wilson Wen Bin Goh, Nanyang Technological University (NTU), and co-investigators Dr Jimmy Lee, Institute of Mental Health (IMH) Singapore , and Professor Limsoon Wong, National University of Singapore (NUS).
Project team contacts:
Read about the Catalyst funds here: