PhoBERT: Application in Disease Classification based on Vietnamese Symptom Analysis
Author:
Nguyen Hai Thanh1ORCID, Huynh Tuyet Ngoc1, Mai Nhi Thien Ngoc1, Le Khoa Dang Dang2, Thi-Ngoc-Diem Pham1ORCID
Affiliation:
1. 1 College of Information and Communication Technology , Can Tho University , Can Tho , Vietnam 2. 2 Information Technology Centre (Area 5), Vietnam Posts and Telecommunications Group , Tien Giang , Vietnam
Abstract
Abstract
Besides the successful use of support software in cutting-edge medical procedures, the significance of determining a disease early signs and symptoms before its detection is a growing pressing requirement to raise the standard of medical examination and treatment. This creates favourable conditions, reduces patient inconvenience and hospital overcrowding. Before transferring patients to an appropriate doctor, healthcare staff must have the patient’s symptoms. This study leverages the PhoBERT model to assist in classifying patients with text classification tasks based on symptoms they provided in the first stages of Vietnamese hospital admission. The outcomes of PhoBERT on more than 200 000 text-based symptoms collected from Vietnamese hospitals can improve the classification performance compared to Bag of Words (BOW) with classic machine learning algorithms, and some considered deep learning architectures such as 1D-Convolutional Neural Networks and Long Short-Term Memory. The proposed method can achieve promising results to be deployed in automatic hospital admission procedures in Vietnam.
Publisher
Walter de Gruyter GmbH
Reference29 articles.
1. S. Thi Thao Nguyen, E. Yamamoto, M. Thi Ngoc Nguyen, H. Bao Le, T. Kariya, Y. M. Saw, C. Duc Nguyen, and N. Hamajima, “Waiting time in the outpatient clinic at a national hospital in Vietnam,” Nagoya J. Med. Sci., vol. 80, no. 2, pp. 227–239, May 2018. 2. D. Q. Nguyen and A. T. Nguyen, “PhoBERT: Pre-trained language models for Vietnamese,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Nov. 2020, pp. 1037–1042. https://doi.org/10.18653/v1/2020.findings-emnlp.92 3. K. Kowsari, J. Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, “Text classification algorithms: A survey,” Information, vol. 10, no. 4, Art. no. 150, Apr. 2019. https://doi.org/10.3390/info10040150 4. V. Dogra, S. Verma, Kavita, P. Chatterjee, J. Shafi, J. Choi, and M. F. Ijaz, “A complete process of text classification system using state-of-the-art NLP models,” Computational Intelligence and Neuroscience, vol. 2022, Art. no. 1883698, Jun. 2022. https://doi.org/10.1155/2022/1883698 5. S. Chua, F. Coenen, and G. Malcolm, “Classification inductive rule learning with negated features,” in L. Cao, Y. Feng, and J. Zhong, Eds. Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science, vol. 6440. Springer, Berlin, Heidelberg, 2010, pp. 125–136. https://doi.org/10.1007/978-3-642-17316-5_12
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|