Mental Health
Mental health is a critical focus for medical research in Singapore. Studies and national health surveys show that poor mental health is increasingly common in Singapore, with rates rising from 12.5% in 2017 to 15.4% in 2024 (National Population Health Survey 2024). Although help-seeking behaviour is improving, mental health services remain under pressure due to rising demand.
The issue of supporting mental health in society sits at the intersection of public health impact, societal change, and economic sustainability—and the pressures affecting mental well-being are intensifying.
Research can help refine treatments, develop community interventions, improve service delivery models, and guide policy-making, ensuring they address real-world needs and produce measurable improvements in outcomes. Robust research informs clinical guidelines, early intervention strategies, and national initiatives such as community mental health teams and school-based programmes thus allowing resources to be directed toward treatments and prevention strategies that actually work for the population.
Sub-themes:
- Depression
- Dementia
- Mental health detection
- Intervention
1 . SoundKeepers Programme: Preventing depression among seniors PI: Prof. Guan Cuntai
2. Multimodal speech parameters for mental health. PI: Asst. Prof. Wilson Goh
3. Computational Neuro-genetic Modelling for Diagnosis and Prognosis in Mental Health. PI: Asst. Prof Wilson Goh
4. Personalised Care and Timely Interventions to Support People Living with Dementia: How technology can help. PI: Asst. Prof. Wilson Goh
1. Jiang, M., Zhang, W., Ding, Y., Teo, K. a. C., Fong, L., Zhang, S., Guo, Z., Liu, C., Bhuvanakantham, R., Sim, W. K. J., Foo, C. H. V., Chua, R. H. J., Padmanabhan, P., Leong, V., Lu, J., Gulyás, B., & Guan, C. (2026). Decoding Covert Speech from EEG by Functional Areas Spatio-Temporal Transformer. IEEE Journal of Biomedical and Health Informatics, PP, 1–14. https://doi.org/10.1109/jbhi.2026.3653025
2. Yee, J. Y., Phua, S., See, Y. M., Andiappan, A. K., Goh, W. W. B., & Lee, J. (2025). Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia. Translational Psychiatry, 15(1), 51. https://doi.org/10.1038/s41398-025-03264-z
3. Kabir, M. N., Wang, L. R., & Goh, W. W. B. (2025). Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning. PLoS Computational Biology, 21(1), e1012716. https://doi.org/10.1371/journal.pcbi.1012716