ERNIE based intelligent triage system

Author:

Wang Chuantao12,Feng Fan12

Affiliation:

1. School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China

2. Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing, China

Abstract

 With the development of Internet+medicine, online medical treatment has gradually become the new development direction of medical industry. Many hospitals provide online registration services to the public, and due to the lack of professional medical knowledge of patients, the problem of wrong registration often occurs. How to use deep learning technology to provide professional help to patients and reduce the waste of medical resources has become an urgent problem. To address the above problems, this paper proposes an ERNIE-based text classification model for intelligent triage. The model consists of two parts, ERNIE and BiGRU. The pre-training model ERNIE is used to extract the feature representation of the text, and then input to the BiGRU neural network to get the text classification results. Compared with different models on 2 datasets, the experimental results show that the model proposed in this paper has better accuracy and recall than other models.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference29 articles.

1. Meng Qun, Yin Xin and Liang Chen, Current situation and reflection on the development of Internet healthcare in China[J], Chinese Journal of Health Information Manament 13(04) (2016), 356–363.

2. China Internet Network Information Center, The 46th Statistical Report on Internet Development in China [EB/OL], (2020-09-29).

3. Zhang Shihong, Ju Wensheng and Shen Tao, Development prospect of Internet medical treatment under epidemic situation [J], China Digital Medicine 15(09) (2020), 15–17+48.

4. Kim Y. , Convolutional Neural Networks for Sentence Classification[J], Eprint Arxiv, 2014.

5. Extensions of recurrent neural network language model [C];Mikolov;Proceedings of the 2011 International Conference on Acoustics, Speech and Signal Processing, Piscataway:IEEE,2011

Cited by 1 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

1. SADE: A Speaker-Aware Dual Encoding Model Based on Diagbert for Medical Triage and Pre-Diagnosis;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3