Automated Triaging Medical Referral for Otorhinolaryngology Using Data Mining and Machine Learning Techniques

Wee, Chee Keong and Zhou, XuJuan and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Chen, Jennifer and Gururajan, Rashmi and Wee, Nathan and Barua, Prabal Datta ORCID: https://orcid.org/0000-0001-5117-8333 (2022) Automated Triaging Medical Referral for Otorhinolaryngology Using Data Mining and Machine Learning Techniques. IEEE Access, 10. pp. 44531-44548.

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Abstract

Public hospitals receive and triage a large volume of medical referrals for otorhinolaryngology annually and it can be a challenge to derive knowledge from them as they are written in unstructured text and may be unavailable in electronic formats. Acquiring knowledge and insights from these referrals are important to public health management and policymakers. Triaging of general practitioner (GP) referrals for ear, nose, and throat (ENT) specialists is a manual process performed by experienced clinicians, but it is time-consuming. This paper proposes utilising machine learning and data mining to automate the process of referrals. In this study, an ensemble of machine learning algorithms to perform clinical text mining against the unstructured referral text in order to derive the relationship among the discovered medical terms was proposed and implemented. A set of comprehensive term sets’ association rules which describe the entire referral dataset’s characteristics was obtained from the association rule mining experiments. The neural network-based text classification model that can classify referrals with high accuracy was developed, tested and reported in this paper.


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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: 10 May 2022 03:02
Last Modified: 09 Oct 2022 23:41
Uncontrolled Keywords: Artificial intelligence application, association rules mining, healthcare application of AI, machine learning and text classification, medical natural language processing, neural network
Fields of Research (2020): 42 HEALTH SCIENCES > 4203 Health services and systems > 420302 Digital health
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460201 Artificial life and complex adaptive systems
42 HEALTH SCIENCES > 4203 Health services and systems > 420308 Health informatics and information systems
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning
42 HEALTH SCIENCES > 4206 Public health > 420699 Public health not elsewhere classified
Socio-Economic Objectives (2020): 20 HEALTH > 2004 Public health (excl. specific population health) > 200499 Public health (excl. specific population health) not elsewhere classified
20 HEALTH > 2002 Evaluation of health and support services > 200299 Evaluation of health and support services not elsewhere classified
20 HEALTH > 2003 Provision of health and support services > 200399 Provision of health and support services not elsewhere classified
20 HEALTH > 2001 Clinical health > 200199 Clinical health not elsewhere classified
Identification Number or DOI: https://doi.org/10.1109/ACCESS.2022.3168980
URI: http://eprints.usq.edu.au/id/eprint/48204

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