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ORIGINAL RESEARCH article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1468519
This article is part of the Research Topic Medical Data Mining and Medical Intelligence Services View all 7 articles

Alleviating the Medical Strain: A Triage Method via Cross-domain Text Classification

Provisionally accepted
Xiao Xiao Xiao Xiao 1Shuqin Wang Shuqin Wang 2Feng Jiang Feng Jiang 3Tingyue Qi Tingyue Qi 1WEI WANG WEI WANG 1*
  • 1 Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu Province, China
  • 2 Yangzhou University, Yangzhou, China
  • 3 First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui Province, China

The final, formatted version of the article will be published soon.

    It is a universal phenomenon for patients who do not know which clinical department to register in large general hospitals. Although triage nurses can help patients, due to the larger number of patients, they have to stand in a queue for minutes to consult. Recently, there have already been some efforts to devote deep-learning techniques or pre-trained language models (PLMs) to triage recommendations. However, these methods may suffer two main limitations: (1) These methods typically require a certain amount of labeled or unlabeled data for model training, which are not always accessible and costly to acquire; (2) These methods have not taken into account the distortion of semantic feature structure and the loss of category discriminability in the model training. To overcome these limitations, in this paper, we propose a cross-domain text classification method based on prompt-tuning, which can classify patients' questions or texts about their symptoms into several given categories to give suggestions on which kind of consulting room patients could choose. Specifically, firstly, different prompt templates are manually crafted based on various data contents, embedding source domain information into the prompt templates to generate another text with similar semantic feature structures for performing classification tasks. Then, five different strategies are employed to expand the label word space for modifying prompts, and the integration of these strategies is used as the final verbalizer.The extensive experiments on Chinese Triage datasets demonstrate that our method achieved state-of-the-art performance.

    Keywords: Medical triage, Cross-domain Text Classification, prompt-tuning, few-shot, Domain adaptation

    Received: 22 Jul 2024; Accepted: 11 Nov 2024.

    Copyright: © 2024 Xiao, Wang, Jiang, Qi and WANG. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: WEI WANG, Affiliated Hospital of Yangzhou University, Yangzhou, 225001, Jiangsu Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.