Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques
Article
Article Title | Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques |
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ERA Journal ID | 44293 |
Article Category | Article |
Authors | Wee, Chee Keong (Author), Zhou, Xujuan (Author), Sun, Ruiliang (Author), Gururajan, Raj (Author), Tao, Xiaohui (Author), Li, Yuefeng (Author) and Wee, Nathan (Author) |
Journal Title | International Journal of Environmental Research and Public Health |
Journal Citation | 19 (12), pp. 1-14 |
Article Number | 7384 |
Number of Pages | 14 |
Year | 2022 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1660-4601 |
1661-7827 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/ijerph19127384 |
Web Address (URL) | https://www.mdpi.com/1660-4601/19/12/7384 |
Abstract | Triaging of medical referrals’ free text can be done 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 proposed the use of machine learning techniques coupling with the clinical prioritization criteria (CPC) of Queensland (QLD) state, Australia to deliver better triaging for referrals in accordance with the CPC’s updates and it doesn’t rely on the past datasets for model training. The medical Natural Language Processing (NLP) was applied in the proposed approach to process the medical text. The proposed multiclass classifier achieved Micro F1 score = 0.98. The proposed approach can help in the processing of 2 million referrals that QLD health service receive annually, therefore they can deliver better health services. |
Keywords | Medical NLP; Triaging; Healthcare AI; Machine learning |
ANZSRC Field of Research 2020 | 420302. Digital health |
420308. Health informatics and information systems | |
460201. Artificial life and complex adaptive systems | |
Byline Affiliations | School of Business |
Department of Health, Queensland | |
School of Mathematics, Physics and Computing | |
Queensland University of Technology | |
University of Queensland | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q76w7/triaging-medical-referrals-based-on-clinical-prioritisation-criteria-using-machine-learning-techniques
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