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Machine learning for biomedicine

Computational biology

We aim to unlook the mistery of life hidden under our genome, cells and orgasms.

Areas:

  • Genomics
  • Drug design
  • Data efficiency

Healthcare

Modern hospitals and medical centres have collected huge amount of clinical data for hundreds of millions of patients over the past decades. However, how to make the best out of the data for improving clinincal services remains the major question. This research aims at characterising the data using statistical models and applying the state-of-the-art machine learning techniques for representation, clustering and prediction both at the patient and the cohort levels.

Areas:

  • Intensive care units (ICU)
  • Mental health
  • Preterm birth
  • Population health
  • Electronic medical records
  • EEG
  • Medical imaging
  • Patient flow



Patents

Talks/Tutorials

Preprints

Publications

  1. Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning, H Le, T Tran, S Venkatesh, KDD'18.
  2. Graph Memory Networks for Molecular Activity PredictionTrang Pham, Truyen Tran, Svetha Venkatesh, ICPR'18.
    • Prelim version appears at NIPS Workshop on Deep learning for physical sciences, 2017.
  3. Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records, P Nguyen, T Tran, S Venkatesh, IJCNN'18.
  4.  Dual control memory augmented neural networks for treatment recommendations, H Le, T Tran, S Venkatesh, PAKDD'18.
  5. 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).
  6. Graph Classification via Deep Learning with Virtual Nodes Trang Pham, Truyen Tran, Hoa Dam, Svetha Venkatesh, Third Representation Learning for Graphs Workshop (ReLiG 2017).
  7. Learning Recurrent Matrix Representation, Kien Do, Truyen Tran, Svetha Venkatesh. Third Representation Learning for Graphs Workshop (ReLiG 2017), also: arXiv preprint arXiv: 1703.01454.
  8. 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]. 
  9. Deepr: A Convolutional Net for Medical Records, Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh, IEEE Journal of Biomedical and Health Informaticsvol. 21, no. 1, pp. 22–30, Jan. 2017, Doi: 10.1109/JBHI.2016.2633963
  10. Stabilizing Linear Prediction Models using Autoencoder, Shivapratap Gopakumara, Truyen Tran, Dinh Phung, Svetha Venkatesh, International Conference on Advanced Data Mining and Applications (ADMA 2016).
  11. 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.
  12. Forecasting patient outflow from wards having no real-time clinical data, Shivapratap Gopakumara, Truyen Tran, Wei Luo, Dinh Phung, Svetha Venkatesh, ICHI'16.
  13. 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.
  14. Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional DataTruyen Tran, Wei Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh, Conference on Machine Learning in Healthcare, LA, USA Aug 2016.
  15. 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).
  16. 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
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. Tensor-variate Restricted Boltzmann Machines, Tu D. Nguyen, Truyen Tran, D. Phung, and S. Venkatesh, AAAI 2015.
  23. 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.
  24. 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 
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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 
  30. 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.
  31. 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
  32. 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.
  33. 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. 
  34. 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.
  35. 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. 

  36. Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data, Truyen Tran, Dinh Q. Phung, Hung H. Bui, and Svetha Venkatesh. In Proc. of 21st Annual Conference on Neural Information Processing Systems, Dec 2008, Vancouver, Canada. [See technical report and thesis for more details and extensions.]

  37. Learning Discriminative Sequence Models from Partially Labelled Data for Activity Recognition, Truyen Tran, Hung Hai Bui, Dinh Q. Phung and Svetha Venkatesh. In Proc. of 10th Pacific Rim International Conference on Artificial Intelligence (PRICAI-08), 15-19 Dec 2008, Hanoi, Vietnam.

  38. AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition, Truyen Tran, Dinh Quoc Phung, Hung Hai Bui, and Svetha Venkatesh. In Proc. of  IEEE Conference on Computer Vision and Pattern Recognition, volume Volume 2, pages 1686-1693, New York, USA, June 2006. 

  39. Boosted Markov networks for activity recognition, Truyen Tran, Hung Hai Bui and Svetha Venkatesh, In Proc. of International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP2005), 5-8 Dec, Melbourne, Australia.