文献类型：期刊文献

中文题名：基于深度学习模型的智能化科室导诊

英文题名：Intelligent department guidance based on deep learning model

作者：顾君杰[1];王蓓[1];李晓禹[2];邹俊忠[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]清影医疗科技(深圳)有限公司研发部,广东深圳518083

年份：2024

卷号：45

期号：1

起止页码：153

中文期刊名：计算机工程与设计

外文期刊名：Computer Engineering and Design

收录：CSTPCD;;北大核心:【北大核心2023】；

基金：国家自然科学基金面上基金项目(61773164)。

语种：中文

中文关键词：科室导诊;多标签;文本预训练;双向门控循环单元;文本分类;深度学习;自然语言处理

外文关键词：department guidance;multi-label;text pre-training;Bi-GRU(bidirectional gated recurrent unit);text classification;deep learning;natural language processing

摘要：为减轻科室导诊人员的工作负荷,对智能化科室导诊的实现方法进行研究。区别于现有的导诊方式,提出一种少参数轻量化的多级科室导诊模型。结合ALBERT预训练解决现有算法参数量过大的问题,并关联多个相关科室,建立ALBERT预训练与Bi-GRU结合的多标签分类模型。通过在互联网医院问诊数据集上的测试,与单科室分类模型对比,验证了该多科室分类模型的预测结果具备可靠性和有效性,能够较好辅助科室导诊工作。
To reduce the workload of traditional department guidance,the intelligent department guidance method was studied.Different from the existing intelligent guidance methods,a multi-department guidance model with fewer parameters was proposed.ALBERT pre-training was implemented to solve the problem of large number of parameters in existing algorithms.A multi-department classification model combining ALBERT pre-training and BI-GRU was constructed.Based on the evaluation using collected consultation data set and the comparison with single-department classification model,it is verified that the presented multi-department model is more reliable and effective,it can better assist the department guidance work.