Dr Wei Luo



Lecturer In Information Technology


Faculty of Sci Eng & Built Env


School of Info Technology


Melbourne Burwood Campus


Doctor of Philosophy, Simon Fraser University, 2008
Master of Engineering, , 2001
Bachelor of Engineering, , 1999


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Comparison of outcomes for cancer patients discussed and not discussed at a multidisciplinary meeting

M Rogers, L Matheson, B Garrard, B Maher, S Cowdery, W Luo, M Reed, S Riches, G Pitson, D Ashley

(2017), Vol. 149, pp. 74-80, Public Health, London, Eng., C1


Toxicity prediction in cancer using multiple instance learning in a multi-task framework

C Li, S Gupta, S Rana, W Luo, S Venkatesh, D Ashley, Q Phung

(2016), Vol. 9651, pp. 152-164, Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I, Berlin, Germany, B1


Consistency of the Health of the Nation Outcome Scales (HoNOS) at inpatient-to-community transition.

W Luo, R Harvey, T Tran, Q Phung, S Venkatesh, J Connor

(2016), Vol. 6, pp. 1-6, BMJ open, London, Eng., C1


Predicting risk of suicide attempt using history of physical illnesses from electronic medical records

C Karmakar, W Luo, T Tran, M Berk, S Venkatesh

(2016), Vol. 3, pp. 1-10, JMIR mental health, Toronto, Ont., C1


Traffic identification in big internet data

B Wang, J Zhang, Z Zhang, W Luo, D Xia

(2016), pp. 129-156, Big data concepts, theories, and applications, Berlin, Germany, B1


Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?

W Luo, E Huning, T Tran, D Phung, S Venkatesh

(2016), Vol. 2, pp. 1-15, Heliyon, Amsterdam, The Netherlands, C1


Forecasting daily patient outflow from a ward having no real-time clinical data

S Gopakumar, T Tran, W Luo, D Phung, S Venkatesh

(2016), Vol. 4, pp. 1-16, JMIR medical informatics, Toronto, Ont., C1


Preterm birth prediction : deriving stable and interpretable rules from high dimensional data

T Tran, W Luo, Q Phung, J Morris, K Rickard, S Venkatesh

(2016), pp. 1-13, MLHC 2016 : Proceedings on Conference on Machine Learning in Healthcare, Los Angeles, California, E1


Guidelines for developing and reporting machine learning predictive models in biomedical research : a multidisciplinary view

W Luo, Q Phung, T Tran, S Gupta, S Rana, C Karmakar, A Shilton, J Yearwood, N Dimitrova, T Ho, S Venkatesh, M Berk

(2016), Vol. 18, pp. 1-10, Journal of medical internet research, Toronto, Ont., C1


Exceptional contrast set mining: moving beyond the deluge of the obvious

D Nguyen, W Luo, D Phung, S Venkatesh

(2016), Vol. LNAI 9992, pp. 455-468, AI 2016 : Advances in artificial intelligence : Proceedings of the 29th Australian Joint Conference, Hobart, Tas., E1


Forecasting patient outflow from wards having no real-time clinical data

S Gopakumar, T Tran, W Luo, D Phung, S Venkatesh

(2016), pp. 177-183, ICHI 2016: Proceedings of the IEEE International Conference on Healthcare Informatics, Chicago, Illinois, E1


Stabilized sparse ordinal regression for medical risk stratification

T Tran, D Phung, W Luo, S Venkatesh

(2015), Vol. 43, pp. 555-582, Knowledge and information systems: an international journal, Berlin, Germany, C1


Web search activity data accurately predict population chronic disease risk in the USA

T Nguyen, T Tran, W Luo, S Gupta, S Rana, Q Phung, M Nichols, L Millar, S Venkatesh, S Allender

(2015), Vol. 69, pp. 693-699, Journal of epidemiology and community health, London, Eng., C1


Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

W Luo, T Nguyen, M Nichols, T Tran, S Rana, S Gupta, Q Phung, S Venkatesh, S Allender

(2015), Vol. 10, pp. 1-13, PLoS One, San Francisco, Calif., C1


Understanding toxicities and complications of cancer treatment: a data mining approach

D Nguyen, W Luo, S Venkatesh, Q Phung

(2015), Vol. 9457, pp. 431-443, AI 2015: Advances in artificial intelligence. 28th Australasian Joint Conference Canberra, ACT, Australia, November 30 - December 4, 2015 Proceedings, Berlin, Germany, B1


Robust traffic classification with mislabelled training samples

B Wang, J Zhang, Z Zhang, W Luo, D Xia

(2015), pp. 328-335, ICPADS 2015: Proceedings of the IEEE Parallel and Distributed Systems 2015 International Conference, Melbourne, Vic., E1


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, R Kennedy, S Venkatesh

(2014), Vol. 14, pp. 1-9, BMC psychiatry, London, Eng., C1


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, R Kennedy, A Broad, D Campbell, D Kipp, M Singh, M Khasraw, L Matheson, D Ashley, S Venkatesh

(2014), Vol. 4, pp. 1-7, BMJ open, London, England, C1


Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

S Rana, T Tran, W Luo, D Phung, R Kennedy, S Venkatesh

(2014), Vol. 38, pp. 377-382, Australian health review, Melbourne, Vic., C1


Detecting contaminated birthdates using generalized additive models

W Luo, M Gallagher, B Loveday, S Ballantyne, J Connor, J Wiles

(2014), Vol. 15, pp. 1-9, BMC bioinformatics, London, England, C1


iPoll: Automatic polling using online search

T Nguyen, D Phung, W Luo, T Tran, S Venkatesh

(2014), Vol. 8786, pp. 266-275, Web Information Systems Engineering ? WISE 2014, Berlin, Germany, B1


A framework for feature extraction from hospital medical data with applications in risk prediction

T Truyen, W Luo, P Dinh, S Gupta, S Rana, R Kennedy, A Larkins, S Venkatesh

(2014), Vol. 15, pp. 1-9, BMC Bioinformatics, London, Eng., C1


Individualized arrhythmia detection with ECG signals from wearable devices

B Nguyen, W Lou, T Caelli, S Venkatesh, D Phung

(2014), pp. 570-576, DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics, Shanghai, China, E1


Unsupervised inference of significant locations from WiFi data for understanding human dynamics

T Nguyen, T Nguyen, W Luo, Venkatesh, Q Phung

(2014), pp. 232-235, MUM 2014 : Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, Melbourne, Victoria, E1-1


Speed up health research through topic modeling of coded clinical data

W Luo, Q Phung, T Nguyen, T Tran, S Venkatesh

(2014), pp. 1-4, IAPR 2014 : Proceedings of 2nd International Workshop on Pattern Recognition for Healthcare Analytics, Stockholm, Sweden, E1-1


Parameter-free search of time-series discord

W Luo, M Gallagher, J Wiles

(2013), Vol. 28, pp. 300-310, Journal of computer science and technology, Berlin, Germany, C1


Estimating the intensity of ward admission and its effect on emergency department access block

W Luo, J Cao, M Gallagher, J Wiles

(2013), Vol. 32, pp. 2681-2694, Statistics in medicine, London, England, C1


An integrated framework for suicide risk prediction

T Tran, Q Phung, W Luo, R Harvey, M Berk, S Venkatesh

(2013), pp. 1410-1418, KDD'13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Chicago, Ill., E1-1


Faster and parameter-free discord search in quasi-periodic time series

W Luo, M Gallagher

(2011), pp. 135-148, PAKDD 2011 : Advances in Knowledge Discovery and Data Mining : Proceedings of PAKDD 2011, Shenzhen, China, E1-1


Unsupervised DRG upcoding detection in healthcare databases

W Luo, M Gallagher

(2010), pp. 600-605, ICDMW 2010 : Proceedings of 10th IEEE International Conference on Data Mining Workshops, Sydney, New South Wales, E1-1


Visualising a state-wide patient data collection : a case study to expand the audience for healthcare data

W Luo, M Gallagher, D O'Kane, J Connor, M Dooris, C Roberts, L Mortimer, J Wiles

(2010), pp. 45-52, HIKM 2010 : Proceedings of the 4th Australasian Workshop on Health Informatics and Knowledge Management, Brisbane, Queensland, E1-1


Mind change optimal learning of Bayes net structure from dependency and independency data

O Schulte, W Luo, R Greiner

(2010), Vol. 208, pp. 63-82, Information and computation, Amsterdam, The Netherlands, C1-1


A new hybrid method for Bayesian network learning With dependency constraints

O Schulte, G Frigo, R Greiner, W Luo, H Khosravi

(2009), pp. 53-60, CIDM 2009 : Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Data Mining, Nashville, Tennesee, E1-1


Mind change optimal learning of Bayes net structure

O Schulte, W Luo, R Greiner

(2007), pp. 187-202, COLT 2007 : Proceedings of the 20th Annual Conference on Learning Theory 2007, San Diego, California, E1-1


Learning Bayesian networks in Semi-deterministic systems

W Luo

(2006), pp. 230-241, Canadian AI 2006 : Advances in artificial intelligence : 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Quebec City, Que?bec, Canada, June 7-9, 2006 : proceedings, Quebec City, Quebec, E1-1


Mind change efficient learning

W Luo, O Schulte

(2006), Vol. 204, pp. 989-1011, Information and computation, Amsterdam, The Netherlands, C1-1


Mind change efficient learning

W Luo, O Schulte

(2005), pp. 398-412, COLT 2005 : Learning Theory : 18th annual conference on learning theory, COLT 2005 Bertinoro, Italy June 27-30, 2005 : proceedings, Bertinoro, Italy, E1-1


Compute inclusion depth of a pattern

W Luo

(2005), pp. 689-690, Learning Theory : 18th annual conference on learning theory, COLT 2005 Bertinoro, Italy June 27-30, 2005 : proceedings, Bertinoro, Italy, E1-1


Funded Projects at Deakin

Australian Competitive Grants

Preventing obesity and promoting healthy body image in Australian secondary schools: a web-based system tailored to individual needs

Prof Jo Williams, Prof Craig Taylor, Prof Susan Sawyer, Prof Marj Moodie, A/Prof Liliana Orellana, Dr Wei Luo, A/Prof Kylie Hesketh, Dr Denise Wilfley

NHMRC Project Grant

  • 2018: $27,900
  • 2017: $228,521

ARC Research Hub for Digital Enhanced Living

Prof John Grundy, Prof Svetha Venkatesh, Prof Anthony Maeder, Prof Kon Mouzakis, Prof Alison Hutchinson, Prof Michael Berk, Prof Ralph Maddison, Prof Abbas Kouzani, Prof Rajesh Vasa, Prof Rafael Calvo, Prof Helen Christensen, Prof Patricia Williams, Prof Dinh Phung, Prof John Yearwood, Prof Susan Gordon, Prof David Powers, Prof Nilmini Wickramasinghe, A/Prof Niranjan Bidargaddi, Dr Santu Rana, Dr Truyen Tran, Dr Sunil Gupta, Dr Wei Luo, A/Prof Mohamed Abdelrazek, Dr Felix Tan, Prof Henning Langberg, A/Prof Lars Kayser, A/Prof Finn Kensing, Prof Freimut Bodendorf, Prof John Hansen, Prof James Warren, Dr Roopak Sinha, Mr Ian Aitken, Prof A Smeaton, Mr Fonda Voukelatos, Mr Jeffrey Fiebig, Mr Dean Serroni, Mr Christopher Farguhar, Mr Ramesh Nagarajan, Ms Jennifer Biggin, Mr John Fouyaxis, David Varley

ARC Industrial Transformation Research Hubs

  • 2017: $601,698

Industry and Other Funding

Unlocking factors for successful job placement through machine learning

Prof Svetha Venkatesh, Dr Wei Luo

  • 2017: $78,028


No completed student supervisions to report