Research Highlights

Improving the mental health of patients with comorbid chronic diseases: A randomized controlled evaluation of the feasibility and preliminary effectiveness of a single-session solution-focused consultation (SSC) in a primary care setting

Funded by: Singapore National Medical Research Council (NMRC)

Lead: A/P Andy Ho (NTU) | Co: Leads: A/P Andy Khong (NTU) & A/P Doreen Tan (NUS)

CADENCE’s Artificial Intelligence, Digital Health, and Human Potential Platform leverages the man-machine interface – the power of technology and data – to deliver lifestyle and behavioural coaching in tandem with clinician-pharmacists trained to manage hyperlipidaemia.

The effective management of hyperlipidaemia, a key modifiable cardiovascular risk factor, through ensuring adequate dosing and adherence to medicines, is key in the primary prevention of cardiovascular disease (CVD).

This platform aims to fulfil several objectives, including: 

• Advancing the development and implementation of artificial intelligence technologies in healthcare to improve treatment of chronic conditions and thus prevention of CVD

• Promoting the use of digital health technologies such as wearables, mobile apps, and telemedicine to increase access to healthcare, to monitor and manage chronic conditions, and to improve patient outcomes

This platform aims to improve healthcare outcomes through empowering individuals and communities to live healthier and more fulfilling lives, thereby leading to better management of chronic conditions and improved quality of life, ultimately reducing healthcare costs.

The rapidly changing facet of society has imposed strain and pressures amongst young people. This can lead to mental illness, which in turn, imposes social and economic challenges. While therapeutic practices such as mindfulness training has been implemented in New Zealand, a data-centric approach is proposed for early detection and to achieve mechanistic understanding.  Mental illnesses have risen rapidly, ranking from 13 in 1990 to 7 in 2017 as a leading cause of DALYS (Disability-adjusted Life Years). About 75% of mental illnesses have their onset before age 25 and continue to afflict prolonged suffering, causing chronic disability. Mental illnesses rank the second leading cause of years lived with disability (YLD). A young person may be diagnosed as Ultra-High Risk (UHR) for development of mental illness, based on a complex panel of behavioural traits.

Using new AI, data science, language and behavioral analysis techniques, 600 youths will be examined to identify social, biological, clinical, and cognitive factors involved in the transition to psychosis in at-risk youth. By combining multiple modalities together with personalized AI modelling techniques, we aim to uncover the molecular basis of mental well-being. Although this project is focused on the high-dimensional -omics technologies, spanning gene, protein and metabolite profiling, we also hypothesize early-stage patients who may manifest changes in mental states, particularly emotions and communications. Language, text, and behaviors are analyzed as novel modalities, which can enrich AI models, but also provide deeper understanding of how changes in behaviour and speech precedes adverse changes in mental health.

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 methods for accurate diagnosis/prognosis of mental illness and suggest optimal interventions.

 The main outcomes:

• Development of new machine-learning/AI methods for multimodal data modelling.
• Better clinical intervention via early prognosis and diagnosis of mental health issues in at-risk youth.
• Developing personalised modelling for a better understanding of individual factors that trigger mental illnesses.
  

Novel personalised modelling technique will be proposed, based on a new clustering approach for selecting a subset of informative datasets. This creates personalised profiling and enhance the classification and prediction of an individual cognitive state. 

The integrated datasets will be modelled using a 3-dimensional structure of artificial spiking neurons to map the spatial information of the data while learning from the temporal patterns “hidden” in the longitudinal measurements. Multimodal data collection, data processing, data integration, and computational modelling will be proposed. These can solve the issue of data integration when dealing with several big dimensional data spaces (genes, biomedical and cognitive) from a cohort study. It also allows our advanced deep learning techniques to learn from multimodal data domains and improve accuracy. Findings are theoretically relevant to enhance the understanding of mental illnesses in New Zealand and worldwide.

Lead: Assistant Professor Wilson Goh

This project is funded under the Government-wide New Zealand-Singapore Enhanced Partnership, the Ministry of Business, Innovation & Employment (MBIE) has established a jointly-funded Data Science Research Programme with the Singapore Data Science Consortium (SDSC), on behalf of the National Research Foundation of Singapore.

Assistant Prof Shannon Ang (NTU) | Co-investigators: Angelique Chan (Duke-NUS and NUS), Rahul Malhotra (Duke-NUS), and Natalie Pang (NUS)

The use of digital communication technologies have proliferated, but we do not fully understand how older adults mix online and offline ways to stay socially connected. This research project seeks to understand the factors that drive online social participation among older adults, and how their online-offline mix of social activities shape their health outcomes. At the same time, the project aims to establish if biases in online non-probability samples of older adults can be reasonably corrected for using information about their social participation.

Funding by MOE Academic Research Fund Tier 2

A Randomized Controlled Evaluation of the Feasibility and Preliminary Effectiveness of a Single-Session Solution-Focused Consultation (SSC) in a Primary Care Setting.

Funded by Singapore National Medical Research Council (NMRC)

Project RESET: Redirecting immune, lipid and metabolic drivers of early cardiovascular disease

Prof Yohanes Eko Riyanto (Co-Investigator) oversees Theme 4 of the project, “Randomized controlled trial (RCT) of digitally supported lifestyle intervention, incorporating behavioural, implementation and economic evaluations.