<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

PhoBERT: Application in Disease Classification based on Vietnamese Symptom Analysis

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

Besides the successful use of support software in cutting-edge medical procedures, the significance of determining a disease early signs and symptoms before...

PhoBERT: Application in Disease Classification based on Vietnamese Symptom Analysis

Online veröffentlicht: 17. Aug. 2023

Seitenbereich: 35 - 43

DOI: https://doi.org/10.2478/acss-2023-0004

Schlüsselwörter
1D-convolutional neural networks, bag of words, long short-term memory, PhoBERT, symptoms, text classification

© 2023 Hai Thanh Nguyen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

PhoBERT: Application in Disease Classification based on Vietnamese Symptom Analysis

Online veröffentlicht: 17. Aug. 2023

Seitenbereich: 35 - 43

DOI: https://doi.org/10.2478/acss-2023-0004

Schlüsselwörter1D-convolutional neural networks, bag of words, long short-term memory, PhoBERT, symptoms, text classification

© 2023 Hai Thanh Nguyen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
1D-convolutional neural networks, bag of words, long short-term memory, PhoBERT, symptoms, text classification