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

Wee, Chee Keong and Zhou, Xujuan and Sun, Ruiliang and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Li, Yuefeng and Wee, Nathan (2022) Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques. International Journal of Environmental Research and Public Health, 19 (12):7384. pp. 1-14. ISSN 1661-7827

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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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 17 Jun 2022 01:26
Last Modified: 22 Jun 2022 02:34
Uncontrolled Keywords: Medical NLP; Triaging; Healthcare AI; Machine learning
Fields of Research (2020): 42 HEALTH SCIENCES > 4203 Health services and systems > 420302 Digital health
42 HEALTH SCIENCES > 4203 Health services and systems > 420308 Health informatics and information systems
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460201 Artificial life and complex adaptive systems
Identification Number or DOI: https://doi.org/10.3390/ijerph19127384
URI: http://eprints.usq.edu.au/id/eprint/49132

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