(Source: TEKsystems)

Home 
Projects
Grants
Policy reports
Patents
Talks
Popular writings
Theses
Publications

 Artificial Intelligence for Health

This research program aims to develop scalable and aligned AI systems that translate into clinically useful tools and inform health policies.

AI agents that see, think, plan, execute, and communicate like human doctors. These capacities rest on goal-driven exploration of vast diagnostic and treatment spaces. Doctor agents work in team with human-in-the-loop to generate hypotheses, take advices, rationalize choices and explain the health science. They are capable of communicating naturally with humans. In particular, the AI Doctors will 

• extract population health patterns and forecast key metrics;
• understand about patient's health, need, preference and intention through patient's diverse sources of data;
• acquire and reason about established medical knowledge; 
• recommend the personalised course of medical actions; 
• support doctors and hospital managers to improve their precision and efficiency; and
• have a meaningful dialog with patients and clinicians.

Grants

• AAA-MEDICAL: Integrating synergistic expertise for better treatment of Abdominal Aortic Aneurysm ($5M), NHMRC Synergy, 2024-2029.
• New biomarkers for abdominal aortic aneurysm ($1M), MRFF Cardiovascular Health Mission, 2023-2026.
• Assessing cerebral palsy risk from video ($350K), Cerebral Palsy Alliance, 2022-2025.
• Optimising treatments in mental health using AI ($5M), MRFF AI in Health, 2021-2026.
• Leveraging digital technology to reduce the prevalence and severity of eating disorders in Australia ($840K), MRFF Million Minds Mission Grant Opportunity, 2019-2022.
  
• ARC research hub for digital enhanced living, ($3M), ARC Industrial Transformation Research Hubs, 2018–2022.
• Advanced data analytics for care management of chronic disease, ($760K), Barwon Health, 2013-2017.
• Improving cancer care ($200K), Western Alliance, 2016-2017.
• Accuracy of machine scoring of fidgety movements from high risk infant populations ($134K), Cerebral Palsy Alliance, 2016–2017.

Policy reports

• Analysis and forecasting of COVID-19 in Ho Chi Minh city. Truyen Tran, Quoc Tran, & Tech4Covid Group. Report to HCM City Council & COVID Response Task Force, 13/08/2021.

Patents

• Medical data processing for risk prediction, Truyen Tran, Santu Rana, Quoc-Dinh Phung, Wei Luo, and Svetha Venkatesh, Provisional patent, June 2013.

Talks/Tutorials

• AI in Covid-19 pandemic, @VANJ Webminar, May 2020.
• Lecture on Deep learning for biomedicine, Southeast Asia Machine Learning (SEA ML) School, Depok, Greater Jakarta, Indonesia, July 2019.
• Deep learning for biomedical discovery and data mining, Tutorial at PAKDD'18, Melbourne, Australia.
• Deep learning for biomedicine, Tutorial at ACML'17 in Seoul, Korea.
• Deep neural nets for healthcare, @Amazon Seattle, Feb 2017

Popular writing

• AI, math, medicine, software, and the sciences: A shifting landscape, Truyen Tran, Medium, August 2024. 
• Living in the future from 2017: AI for healthcare, Truyen Tran, Medium/Blogpost, Feb 2017. 

Preprints

• An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments, T Nguyen, T Tran, S Gopakumar, D Phung, S Venkatesh, arXiv preprint arXiv:1605.01116
• PINNs for medical image analysis: A survey. C Banerjee, K Nguyen, O Salvado, T Tran, C Fookes. arXiv preprint arXiv:2408.01026
  

Publications

• MP-PINN: A Multi-Phase Physics-Informed Neural Network for epidemic forecasting, Thang Nguyen, Dung Nguyen, Kha Pham and Truyen Tran, in Proceedings of the 22nd Australasian Data Science and Machine Learning Conference (AusDM'24), Melbourne, Australia | 25-27 November 2024.
• Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand, N Nghiem, N Wilson, J Krebs, T Tran, BMC Medical Informatics and Decision Making, 2024.
• Fine-grained fidgety movement classification using active learning. R Morais, T Tran, C Alexander, N Amery, C Morgan, A Spittle, V Le, ... IEEE J Biomed Health Informatics, doi: 10.1109/JBHI.2024.
• Vision: Requirements engineering for software development in aged care. J Grundy, A Madugalla, J McIntosh, T Tran, 2023 IEEE 31st International Requirements Engineering Conference Workshops.
• Robust and interpretable general movement assessment using fidgety movement detection, Romero Morais, [...], Truyen Tran, and Svetha Venkatesh, IEEE Journal of Biomedical and Health Informatics, 2023.
• Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: The Vibe Up Study. Kit Huckvale, [...], Truyen Tran, [...], BMJ Open (2023).
• Application of machine learning techniques to identify data reliability and factors affecting outcome after stroke using electronic administrative records, Santu Rana, Wei Luo, Truyen Tran, Svetha Venkatesh, Paul Talman, Thanh G Phan, Dinh Phung, Benjamin B Clissold, Frontiers in Neurology, 2021, doi: 10.3389/fneur.2021.670379.
• A spatio-temporal attention-based model for infant movement assessment from videos, Binh Nguyen-Thai, Vuong Le, C Morgan, N Badawi, Truyen Tran, Svetha Venkatesh, IEEE journal of biomedical and health informatics, 2021.
• Precision Psychiatry with immunological and cognitive biomarkers: A multi-domain prediction for the diagnosis of Bipolar Disorder or Schizophrenia using machine learning, Brisa Fernandes, Chandan Karmakar, Ryad Tamouza, Truyen Tran, Nora Hamdani, Hakim Laouamri, Jean-Romain Richard, Robert Yolken, Michael Berk, Svetha Venkatesh, Marion Leboyer, Translational Psychiatry, 05/2020
• Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning, H Le, T Tran, S Venkatesh, KDD'18.
• Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records, P Nguyen, T Tran, S Venkatesh, IJCNN'18.
• Finding Algebraic Structure of Care in Time: A Deep Learning Approach, Phuoc Nguyen, Truyen Tran, Svetha Venkatesh, NIPS Workshop on Machine Learning for Health (ML4H), 2017.
• Dual control memory augmented neural networks for treatment recommendations, H Le, T Tran, S Venkatesh, PAKDD'18.
• Deep Learning to Attend to Risk in ICU, Phuoc Nguyen, Truyen Tran, Svetha Venkatesh, IJCAI'17 Workshop on Knowledge Discovery in Healthcare II: Towards Learning Healthcare Systems (KDH 2017).
• Predicting healthcare trajectories from medical records: A deep learning approach,Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh, Journal of Biomedical Informatics, April 2017, DOI: 10.1016/j.jbi.2017.04.001. [Tech report PDF]. 
• DeepCare: A Deep Dynamic Memory Model for Predictive Medicine, Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh, PAKDD'16, Auckland, NZ, April 2016. 
• Deepr: A Convolutional Net for Medical Records, Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh, IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 22–30, Jan. 2017, Doi: 10.1109/JBHI.2016.2633963
• Stabilizing Linear Prediction Models using Autoencoder, Shivapratap Gopakumara, Truyen Tran, Dinh Phung, Svetha Venkatesh, International Conference on Advanced Data Mining and Applications (ADMA 2016).
• Guidelines for Developing and Reporting of Machine Learning Predictive Models in Biomedical Research, Wei Luo Dinh Phung; Truyen Tran; Sunil Gupta; Santu Rana; Chandan Karmakar; Alistair Shilton; John Yearwood; Nevenka Dimitrova; Tu Bao Ho; Svetha Venkatesh; Michael Berk, JMIR, 18(12), 2016.
• Forecasting patient outflow from wards having no real-time clinical data, Shivapratap Gopakumara, Truyen Tran, Wei Luo, Dinh Phung, Svetha Venkatesh, ICHI'16.
• Screening for Post 32-Week Preterm Birth Risk: How Helpful is Routine Perinatal Data collection? Wei Luo, Emily Y-S Huning, Truyen Tran, Dinh Phung, Svetha Venkatesh, Heliyon, Volume 2, Issue 6, June 2016, Article e00119.
• Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data,  Truyen Tran, Wei Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh, Conference on Machine Learning in Healthcare, LA, USA Aug 2016.
• Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records, Chandan Karmakar, Wei Luo, Truyen Tran, Michael Berk, and Svetha Venkatesh, JMIR Mental Health (JMH).
• Consistency of the Health of the Nation Outcome Scales (HoNOS) at inpatient-to-community transition, Wei Luo, Richard Harvey, Truyen Tran, Dinh Phung, Svetha Venkatesh, Jason Connor, BMJ Open 2016;6:e010732 doi:10.1136/bmjopen-2015-010732
• Forecasting Daily Patient Outflow From a Ward Having no Real-Time Clinical Data, Shivapratap Gopakumara, Truyen Tran, Wei Luo, Dinh Phung, Svetha Venkatesh, JMIR, Vol 4, No 3 (2016): Jul-Sept.
• Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset, Wei Luo , Thin Nguyen, Melanie Nichols, Truyen Tran, Santu Rana, Sunil Gupta, Dinh Phung, Svetha Venkatesh, Steve Allender. PLoS ONE, May 4, 2015DOI: 10.1371/journal.pone.0125602.
• Stabilizing Sparse Cox Model using Statistic and Semantic Structures in Electronic Medical Records. Shivapratap Gopakumar, Tu Dinh Nguyen, Truyen Tran, Dinh Phung, and Svetha Venkatesh, PAKDD'15, HCM City, Vietnam, May 2015.
• Web search activity data accurately predicts population chronic disease risk in the USA, Thin Nguyen, Truyen Tran, Wei Luo, Sunil Gupta, Santu Rana, Dinh Phung, Melanie Nichols, Lynne Millar, Svetha Venkatesh, Steven Allender, Journal of Epidemiology & Community Health, 2015, doi:10.1136/jech-2014-204523.
• Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (e-NRBM), Truyen Tran, Tu D. Nguyen, D. Phung, and S. Venkatesh, Journal of Biomedical Informatics, 2015, doi:10.1016/j.jbi.2015.01.012.
• Tensor-variate Restricted Boltzmann Machines, Tu D. Nguyen, Truyen Tran, D. Phung, and S. Venkatesh, AAAI 2015.
• A framework for feature extraction from hospital medical data with applications in risk prediction, Truyen Tran, Wei Luo, Dinh Phung, Sunil Gupta, Santu Rana, Richard Lee Kennedy, Ann Larkins and Svetha Venkatesh, BMC Informatics, 2015, DOI:10.1186/s12859-014-0425-8.
• Stabilizing high-dimensional prediction models using feature graphs, Shivapratap Gopakumar, Truyen Tran, Tu Dinh Nguyen, Dinh Phung, and Svetha Venkatesh, IEEE Journal of Biomedical and Health Informatics, 2014 DOI:10.1109/JBHI.2014.2353031S 
• Stabilizing sparse Cox model using clinical structures in electronic medical records, S Gopakumar, Truyen Tran, D Phung, S Venkatesh, 2nd International Workshop on Pattern Recognition for Healthcare Analytics, August 2014
• Speed up health research through topic modeling of coded clinical data, Wei Luo, Dinh Phung, Vu Nguyen, Truyen Tran, Svetha Venkatesh, 2nd International Workshop on Pattern Recognition for Healthcare Analytics, August 2014
• iPoll: Automatic polling using online search, T Nguyen, D Phung, W Luo, Truyen Tran, S Venkatesh, Proc. of 15th International Conference on Web Information System Engineering (WISE), 2014
• HealthMap: A visual platform for patient suicide risk review, S Rana, W Luo, Truyen Tran, D Phung, S Venkatesh, R Harvey, HISA Big Data, Melbourne April, 2014
• Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data, Santu Rana, Truyen Tran, Wei Luo, Dinh Phung, Richard L. Kennedy, and Svetha Venkatesh, Australian Health Review, 2014 doi:dx.doi.org/10.1071/AH14059 
• Risk stratification using data from electronic medical records better predict suicide risks than clinician assessments, Truyen Tran, Wei Luo, Dinh Phung, Richard Harvey, Michael Berk, Richard Lee Kennedy, Svetha Venkatesh, BMC Psychiatry, 14:76, 2014.
• Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry, Sunil Gupta, Truyen Tran, Wei Luo, Dinh Phung, Richard Lee Kennedy, Adam Broad, David Campbell, David Kipp, Madhu Singh, Mustafa Khasraw, Leigh Matheson, David M Ashley, Svetha Venkatesh, BMJ Open, 2014, doi:10.1136/bmjopen-2013-004007
• Stabilized sparse ordinal regression for medical risk stratification, Truyen Tran, Dinh Phung, Wei Luo, and Svetha Venkatesh, Knowledge and Information Systems, 2014, DOI: 10.1007/s10115-014-0740-4.
• An integrated framework for suicide risk prediction, Truyen Tran, Dinh Phung, Wei Luo, Richard Harvey, Michael Berk, and Svetha Venkatesh, In Proc. of 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Chicago, USA, August, 2013. 
• Latent patient profile modelling and applications with Mixed-Variate Restricted Boltzmann Machine, Tu D. Nguyen, Truyen Tran, D. Phung, and S. Venkatesh,  In Proc. of 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’13), Gold Coast, Australia, April 2013.
• Classification and Pattern Discovery of Mood in Weblogs, Thin Nguyen, Dinh Q. Phung, Brett Adams, Truyen Tran and Svetha Venkatesh. In Proc. of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 21-24 June, Hyderabad, India, 2010