With the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

中文翻译：

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基于中医BERT的基于云的智能自诊断和科室推荐服务

近年来，随着医院信息化和互联网医疗服务的飞速发展，大多数医院都启动了在线医院预约挂号系统，以消除患者排队，提高医疗服务效率。但是，大多数患者缺乏专业医学知识，并且在注册时不知道如何选择科室。为了指导患者有效地就医和注册，我们提出了CIDRS，它是一种基于云计算环境中基于变压器的中文医学双向编码器表示的智能的自诊断和部门推荐框架。我们还建立了在大规模中文医学语料库上训练的中文BERT模型（CHMBERT）。该模型用于优化自我诊断和部门推荐任务。为了解决终端的有限计算能力，我们在容器和微服务技术的基础上将提出的框架部署在了云计算环境中。实验使用了来自医院的真实医学数据集，结果表明，该模型在性能方面优于传统的深度学习模型和其他预训练的语言模型。