Xiaohui Tao

University of Southern Queensland, Department of Mathematics and Computing, Faculty of Sciences, Faculty Member

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Dr. Xiaohui (Daniel) Tao is a lecturer of computing in the Department of Mathematics and Computing, Faculty of Sciences at the University of Southern Queensland (USQ), Australia. Before working at USQ, he was a research associate with the Computer Science Discipline, Faculty of Science and Technology at Queensland University of Technology (QUT), Australia, after receiving his PhD degree from QUT in 2009.

His research interests include ontology learning and mining, knowledge engineering, web intelligence, data mining, sentiment analysis and opinion mining, machine learning, and information retrieval. He has published his research results on many top journals (e.g., TKDE) and conferences (e.g., CIKM, TREC, KSEM, and Web Intelligence). You may find some of his published papers on his personal website linked below.

Dr. Tao has been a Co-Organizer of the WI-IAT Doctoral Workshop series for 2008, 2009, and 2010 and the International Workshop on Web Personalization and Recommender Systems in 2010 and 2011. He has also been a Publicity Chair of International Conferences on Active Media Technology and on Brain Informatics in 2010 and 2011, and International Workshop on Topic Feature Discovery and Opinion Mining in 2010. On top of these, he is a regular reviewer of many high-ranking international journals and a PC member in many International conferences and workshops.
Supervisors: Prof Yuefeng Li

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Papers by Xiaohui Tao

Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques

International Journal of Environmental Research and Public Health

Triaging of medical referrals can be completed using various machine learning techniques, but tra... more Triaging of medical referrals can be completed using various machine learning techniques, but trained models with historical datasets may not be relevant as the clinical criteria for triaging are regularly updated and changed. This paper proposes the use of machine learning techniques coupled with the clinical prioritisation criteria (CPC) of Queensland (QLD), Australia, to deliver better triaging for referrals in accordance with the CPC’s updates. The unique feature of the proposed model is its non-reliance on the past datasets for model training. Medical Natural Language Processing (NLP) was applied in the proposed approach to process the medical referrals, which are unstructured free text. The proposed multiclass classification approach achieved a Micro F1 score = 0.98. The proposed approach can help in the processing of two million referrals that the QLD health service receives annually; therefore, they can deliver better and more efficient health services.

Xiaohui Tao

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