Truyen Tran - Google Scholar Citations

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	All	Since 2015
Citations	2440	2226
h-index	26	25
i10-index	57	50

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Svetha VenkateshProfessor of Computer Science, Deakin UniversityVerified email at deakin.edu.au
Dinh PhungProfessor, Monash UniversityVerified email at monash.edu
Wei LuoDeakin UniversityVerified email at deakin.edu.au
Trang PhamSoftware Engineer, GoogleVerified email at google.com
Aditya GhoseProfessor of Computer Science, University of WollongongVerified email at uow.edu.au
Hoa Khanh DamAssociate Professor in Software Engineering, University of WollongongVerified email at uow.edu.au
Sunil GuptaAssociate Professor, Deakin UniversityVerified email at deakin.edu.au
Santu RanaAssociate Professor of Computer Science, Deakin UniversityVerified email at deakin.edu.au
Tu Dinh NguyenSenior Research Scientist, Trusting SocialVerified email at trustingsocial.com
Michael BerkDeakin UniversityVerified email at deakin.edu.au
Hung BuiResearch Scientist, Google DeepMindVerified email at google.com
Phuoc NguyenAssociate research fellow, Deakin UniversityVerified email at deakin.edu.au
Thin NguyenSenior Research Fellow, Deakin University, AustraliaVerified email at deakin.edu.au
Morakot Choetkiertikul, Ph.D.Lecture at Faculty of ICT, Mahidol UniversityVerified email at mahidol.ac.th
Kien DoDeakin UniversityVerified email at deakin.edu.au
John GrundyAustralian Laureate Fellow and Professor of Software Engineering, Faculty of IT, Monash UniversityVerified email at monash.edu
Chandan KarmakarLecturer of Computer Science, Deakin UniversityVerified email at deakin.edu.au
Hung LeDeakin UniversityVerified email at deakin.edu.au
Prof Richard J HarveyClinical Professor, Deakin UniversityVerified email at deakin.edu.au
Shivapratap GopakumarDeakin UniversityVerified email at deakin.edu.au

Truyen Tran

Associate Professor, Applied AI Institute, Deakin University

Verified email at deakin.edu.au - Homepage

artificial intelligence machine learning computational biomedicine


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
DeepCare: A deep dynamic memory model for predictive medicine T Pham, T Tran, D Phung, S Venkatesh PAKDD 6, 26094, 2016	169	2016
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	167*	2017
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	151	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	129	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	97	2015
Column networks for collective classification T Pham, T Tran, D Phung, S Venkatesh AAAI'17, 2017	86	2017
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	86	2010
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	85	2014
Ordinal Boltzmann machines for collaborative filtering TT Truyen, DQ Phung, S Venkatesh UAI'09 (Best paper runner up), 548-556, 2009	64	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	52	2014
A deep language model for software code HK Dam, T Tran, T Pham FSE'16 Workshop on Naturalness of Software (NL+SE), 2016	48	2016
Stabilized sparse ordinal regression for medical risk stratification T Tran, D Phung, W Luo, S Venkatesh Knowledge and Information Systems, 2015	48	2015
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	46*	2018
Mixed-variate restricted Boltzmann machines T Tran, D Phung, S Venkatesh ACML'11, 2011	42	2011
Hierarchical semi-Markov conditional random fields for recursive sequential data T Tran, D Phung, H Bui, S Venkatesh NIPS'08, 2008	42	2008
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	40*	2019
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	39	2018
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	39*	2014
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	38	2014
Improving generalization and stability of Generative Adversarial Networks H Thanh-Tung, T Tran, S Venkatesh ICLR'19, 2019	35	2019

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