Automated Triaging Medical Referral for Otorhinolaryngology Using Data Mining and Machine Learning Techniques
Contribution to Journal
Article Title | Automated Triaging Medical Referral for Otorhinolaryngology Using Data Mining and Machine Learning Techniques |
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ERA Journal ID | 210567 |
Authors | Wee, Chee Keong (Author), Zhou, XuJuan (Author), Gururajan, Raj (Author), Tao, Xiaohui (Author), Chen, Jennifer (Author), Gururajan, Rashmi (Author), Wee, Nathan (Author) and Barua, Prabal Datta (Author) |
Journal Title | IEEE Access |
Journal Citation | 10, pp. 44531-44548 |
Number of Pages | 18 |
Year | 2022 |
Publisher | IEEE |
Place of Publication | United States |
ISSN | 2169-3536 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2022.3168980 |
Web Address (URL) | https://ieeexplore.ieee.org/document/9760418 |
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. |
Keywords | Medical diagnostic imaging; Australia; Data mining; Biological neural networks; Natural language processing; Machine learning algorithms; Licenses; Artificial intelligence application; association rules mining; healthcare application of AI; machine learning and text classification; medical natural language processing; neural network |
ANZSRC Field of Research 2020 | 420302. Digital health |
460201. Artificial life and complex adaptive systems | |
420308. Health informatics and information systems | |
460208. Natural language processing | |
461103. Deep learning | |
420699. Public health not elsewhere classified | |
Byline Affiliations | School of Business |
School of Mathematics, Physics and Computing | |
Department of Health, Queensland | |
Dialog Information Technology, Australia | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7476/automated-triaging-medical-referral-for-otorhinolaryngology-using-data-mining-and-machine-learning-techniques
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