Truyen Tran
Truyen Tran
Associate Professor, Applied AI Institute, Deakin University
Verified email at - Homepage
Cited by
Cited by
Deepr: A convolutional net for medical records
P Nguyen, T Tran, N Wickramasinghe, S Venkatesh
IEEE journal of biomedical and health informatics 21 (1), 22-30, 2017
DeepCare: A deep dynamic memory model for predictive medicine
T Pham, T Tran, D Phung, S Venkatesh
PAKDD 6, 26094, 2016
Guidelines for developing and reporting machine learning predictive models in biomedical research: A multidisciplinary view
W Luo, D Phung, T Tran, S Gupta, S Rana, C Karmakar, A Shilton, ...
Journal of medical Internet research 18 (12), 2016
Predicting healthcare trajectories from medical records: A deep learning approach
T Pham, T Tran, D Phung, S Venkatesh
Journal of Biomedical Informatics 69, 218--229, 2017
Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)
T Tran, TD Nguyen, D Phung, S Venkatesh
Journal of Biomedical Informatics, 2015
Column networks for collective classification
T Pham, T Tran, D Phung, S Venkatesh
AAAI'17, 2017
Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments
T Tran, W Luo, D Phung, R Harvey, M Berk, RL Kennedy, S Venkatesh
BMC Psychiatry (ECR Best paper awarded by CRESP), 2014
Nonnegative shared subspace learning and its application to social media retrieval
SK Gupta, D Phung, B Adams, T Tran, S Venkatesh
KDD'10, 1169-1178, 2010
Ordinal Boltzmann machines for collaborative filtering
TT Truyen, DQ Phung, S Venkatesh
UAI'09 (Best paper runner up), 548-556, 2009
Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry
S Gupta, T Tran, W Luo, D Phung, RL Kennedy, A Broad, D Campbell, ...
BMJ Open, 2014
Automatic feature learning for predicting vulnerable software components
HK Dam, T Tran, T Pham, SW Ng, J Grundy, A Ghose
IEEE Transactions on Software Engineering, 2018
Lessons learned from using a deep tree-based model for software defect prediction in practice
HK Dam, T Pham, SW Ng, T Tran, J Grundy, A Ghose, T Kim, CJ Kim
MSR'19, 2019
A deep language model for software code
HK Dam, T Tran, T Pham
FSE'16 Workshop on Naturalness of Software (NL+SE), 2016
A deep learning model for estimating story points
M Choetkiertikul, HK Dam, T Tran, T Pham, A Ghose, T Menzies
IEEE Transactions on Software Engineering, DOI:10.1109/TSE.2018.2792473, 2018
Stabilized sparse ordinal regression for medical risk stratification
T Tran, D Phung, W Luo, S Venkatesh
Knowledge and Information Systems, 2015
Improving generalization and stability of Generative Adversarial Networks
H Thanh-Tung, T Tran, S Venkatesh
ICLR'19, 2019
Mixed-variate restricted Boltzmann machines
T Tran, D Phung, S Venkatesh
ACML'11, 2011
Hierarchical semi-Markov conditional random fields for recursive sequential data
T Tran, D Phung, H Bui, S Venkatesh
NIPS'08, 2008
Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data
S Rana, T Tran, W Luo, D Phung, S Venkatesh, RL Kennedy
Australian Health Review, 2014
A framework for feature extraction from hospital medical data with applications in risk prediction
T Tran, W Luo, D Phung, S Gupta, S Rana, RL Kennedy, A Larkins, ...
BMC bioinformatics 15 (1), 6596, 2014
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