School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
Digital Application Services, eHealth, Brisbane, QLD 4000, Australia.
Int J Environ Res Public Health. 2022 Jun 16;19(12):7384. doi: 10.3390/ijerph19127384.
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.
分诊可以使用各种机器学习技术来完成，但是使用历史数据集训练的模型可能并不相关，因为分诊的临床标准经常更新和改变。本文提出了使用机器学习技术结合澳大利亚昆士兰州（QLD）的临床优先排序标准（CPC），根据 CPC 的更新，提供更好的分诊服务。所提出模型的独特之处在于它不依赖过去的数据进行模型训练。本研究方法中应用了医学自然语言处理（NLP）来处理非结构化的自由文本医疗转介。所提出的多类分类方法的微观 1 得分为 0.98。该方法有助于处理 QLD 卫生服务部门每年收到的 200 万份转介，从而提供更好、更高效的医疗服务。
