LCMDC: Large-scale Chinese Medical Dialogue Corpora for Automatic Triage and Medical Consultation

• URL: http://arxiv.org/abs/2410.03521v1
• Date: Fri, 27 Sep 2024 00:01:32 GMT
• Title: LCMDC: Large-scale Chinese Medical Dialogue Corpora for Automatic Triage and Medical Consultation
• Authors: Xinyuan Wang, Haozhou Li, Dingfang Zheng, Qinke Peng,
• Abstract summary: The COVID-19 pandemic underscored major deficiencies in traditional healthcare systems. Existing studies face two main challenges. First, the scarcity of large-scale, publicly available, domain-specific medical datasets due to privacy concerns. Second, existing methods lack medical knowledge and struggle to accurately understand professional terms and expressions in patient-doctor consultations.
• Score: 2.04367431902848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The global COVID-19 pandemic underscored major deficiencies in traditional healthcare systems, hastening the advancement of online medical services, especially in medical triage and consultation. However, existing studies face two main challenges. First, the scarcity of large-scale, publicly available, domain-specific medical datasets due to privacy concerns, with current datasets being small and limited to a few diseases, limiting the effectiveness of triage methods based on Pre-trained Language Models (PLMs). Second, existing methods lack medical knowledge and struggle to accurately understand professional terms and expressions in patient-doctor consultations. To overcome these obstacles, we construct the Large-scale Chinese Medical Dialogue Corpora (LCMDC), comprising a Coarse-grained Triage dataset with 439,630 samples, a Fine-grained Diagnosis dataset with 199,600 samples, and a Medical Consultation dataset with 472,418 items, thereby addressing the data shortage in this field. Moreover, we further propose a novel triage system that combines BERT-based supervised learning with prompt learning, as well as a GPT-based medical consultation model using reinforcement learning. To enhance domain knowledge acquisition, we pre-trained PLMs using our self-constructed background corpus. Experimental results on the LCMDC demonstrate the efficacy of our proposed systems.

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