Abstract
AbstractWith 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.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
the Program of Natural Science Research of Jiangsu colleges and universities
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Reference43 articles.
1. Qi L, He Q, Chen F, Dou W, Wan S, Zhang X, Xu X (2019) Finding all you need: web apis recommendation in web of things through keywords search. IEEE Trans Comput Soc Syst 6(5):1063–1072.
2. Zhang S, Choo K-KR, Liu Q, Wang G (2018) Enhancing privacy through uniform grid and caching in location-based services. Futur Gener Comput Syst 86:881–892.
3. Liu H, Kou H, Yan C, Qi L (2020) Keywords-driven and popularity-aware paper recommendation based on undirected paper citation graph. Complexity 2020:1–15.
4. Devlin J, Chang M-W, Lee K, Toutanova K (2019) Bert: Pre-training of deep bidirectional transformers for language understanding In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 4171–4186.. ACL. https://doi.org/10.18653/v1/N19-1423.
5. Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV (2019) Xlnet: Generalized autoregressive pretraining for language understanding In: Advances in Neural Information Processing Systems, 5754–5764.. MIT Press. https://proceedings.neurips.cc/paper/2019/file/dc6a7e655d7e5840e66733e9ee67cc69-Paper.pdf.
Cited by
14 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