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

Journal: International journal of environmental research and public health
Published Date:

Abstract

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 1 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.

Authors

  • Chee Keong Wee
    School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
  • Xujuan Zhou
    Centre for Health Informatics, Australian Institute of Health Innovation, The University of New South Wales, Sydney, NSW 2052, Australia.
  • Ruiliang Sun
    Digital Application Services, eHealth, Brisbane, QLD 4000, Australia.
  • Raj Gururajan
    School of Management and Enterprise, University of Southern Queensland, Springfield 4300, Australia.
  • Xiaohui Tao
    School of Sciences, University of Southern Queensland, Toowoomba 4350, Australia.
  • Yuefeng Li
    School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia.
  • Nathan Wee
    Faculty of Science, University of Queensland, Brisbane, QLD 4072, Australia.