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Machine
learning for biomedicine Computational biology We aim to unlock the mistery of life hidden under our genome, cells and organisms.
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: ICU | Mental health | Preterm birth | Population health | EMR | EEG | Medical imaging | Patient flow
Patents
Talks/Tutorials
- Lecture on Deep learning for biomedicine, Southeast Asia Machine Learning (SEA ML) School, Depok, Greater Jakarta, Indonesia, July 2019.
- Deep learning for genomics: Present and future, Genomic Medicine 2019, Hanoi, Vietnam, June 2019.
- Deep learning for biomedicine: Genomics and Drug design, Institute of Big Data, Hanoi, Vietnam, Jan 2019.
- Tutorial on Deep learning for biomedical discovery and data mining, PAKDD'18, Melbourne, Australia.
- Tutorial on Deep learning for biomedicine at ACML'17 in Seoul, Korea.
- Deep neural nets for healthcare, @Amazon Seattle, 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
- Relational dynamic memory networks, Trang Pham, Truyen Tran, Svetha Venkatesh, arXiv preprint arXiv:1808.04247
- Personalized Annotation-based Networks (PAN) for the prediction of breast cancer relapse, T Nguyen, SC Lee, TP Quinn, B Truong, X Li, T Tran, S Venkatesh, TD Le, bioRxiv, DOI: 10.1101/534628.
Publications
- Deep in the bowel: Highly interpretable neural encoder-decoder networks predict gut metabolites from gut microbiome, V Le, TP Quinn, T Tran, S Venkatesh, BMC Supplements (To appear), 2020.
- DeepTRIAGE:
Interpretable and individualised biomarker scores using attention
mechanism for the classification of breast cancer sub-types. A Beykikhoshk, TP Quinn, SC Lee, T Tran, S Venkatesh, BMC Supplements (To appear), 2020.
- Graph transformation policy
network for chemical reaction prediction, Kien Do, Truyen Tran, Svetha Venkatesh, KDD'19.
- Attentional multilabel learning over graphs: A message passing approach, K Do, T Tran, T Nguyen, S Venkatesh, Machine Learning, 2019.
- Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning, H Le, T Tran, S Venkatesh, KDD'18.
- Graph Memory Networks for Molecular Activity Prediction, Trang Pham, Truyen Tran, Svetha Venkatesh, ICPR'18.
- Prelim version appears at NIPS Workshop on Deep learning for physical sciences, 2017.
- Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records, P Nguyen, T Tran, S Venkatesh, IJCNN'18.
- Prelim version: 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).
- Graph Classification via Deep Learning with Virtual Nodes Trang Pham, Truyen Tran, Hoa Dam, Svetha Venkatesh, Third Representation Learning for Graphs Workshop (ReLiG 2017).
- Learning Recurrent Matrix Representation, Kien Do, Truyen Tran, Svetha
Venkatesh. Third Representation Learning for Graphs Workshop (ReLiG 2017), also: arXiv
preprint arXiv: 1703.01454.
- 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].
- Prelim version: 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.
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