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

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Listed:
  • Chee Keong Wee

    (School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia
    Digital Application Services, eHealth, Brisbane, QLD 4000, Australia)

  • Xujuan Zhou

    (School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia)

  • Ruiliang Sun

    (Digital Application Services, eHealth, Brisbane, QLD 4000, Australia)

  • Raj Gururajan

    (School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia)

  • Xiaohui Tao

    (School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 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)

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 F 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.

Suggested Citation

  • Chee Keong Wee & Xujuan Zhou & Ruiliang Sun & Raj Gururajan & Xiaohui Tao & Yuefeng Li & Nathan Wee, 2022. "Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques," IJERPH, MDPI, vol. 19(12), pages 1-13, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:12:p:7384-:d:840114
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    References listed on IDEAS

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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    2. Hsiang-Chun Dong & Hsiang-Kai Dong & Mu-Hsien Yu & Yi-Hsin Lin & Cheng-Chang Chang, 2020. "Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study," IJERPH, MDPI, vol. 17(16), pages 1-18, August.
    3. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
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