融合医疗实体标签语义的医疗文本分类模型  

Medical text classification model integrating medical entity label semantics

作　　者：韦莉 赵德春[1] 秦璐 刘洋华子 沈宇辰 叶昌荣 WEI Li;ZHAO Dechun;QIN Lu;LIU Yanghuazi;SHEN Yuchen;YE Changrong(School of Life Health Information Science and Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,P.R.China)

机构地区：[1]重庆邮电大学生命健康信息科学与工程学院,重庆400065

出　　处：《生物医学工程学杂志》2025年第2期326-333,共8页Journal of Biomedical Engineering

基　　金：国家自然科学基金青年科学基金项目(62201106);重庆市自然科学基金项目(CSTB2024NSCQ-MSX0957)。

摘　　要：医疗问句的自动分类对提升线上医疗服务的质量和效率具有重大意义,属于意图识别任务。联合实体识别和意图识别优于单任务模型的效果。目前,大多数公开医疗文本意图识别数据集缺乏实体标注,而人工标注这些实体耗时费力。为解决这一难题,本文提出一种融合医疗实体标签语义的医疗文本分类模型—基于转换器循环卷积神经网络实体标签语义的双向编码器表示(BRELS)。该模型首先利用自适应融合机制将医疗实体标签的先验知识融入,实现局部特征增强;然后在全局特征提取中,采用轻量化循环卷积神经网络,既抑制参数的增长,又保留文本的原始语义。本研究在三个公开医疗文本意图识别数据集上进行消融实验和对比实验以验证所提模型的性能;结果显示,该模型在各数据集上的F1值分别达到了87.34%、81.71%和77.74%。研究结果表明,BRELS模型能有效地识别和理解医疗术语,进而有效地识别用户的意图,提高了线上医疗服务的质量和效率。Automatic classification of medical questions is of great significance in improving the quality and efficiency of online medical services, and belongs to the task of intent recognition. Joint entity recognition and intent recognition perform better than single task models. Currently, most publicly available medical text intent recognition datasets lack entity annotation, and manual annotation of these entities requires a lot of time and manpower. To solve this problem, this paper proposes a medical text classification model, bidirectional encoder representation based on transformer-recurrent convolutional neural network-entity-label-semantics(BRELS), which integrates medical entity label semantics. This model firstly utilizes an adaptive fusion mechanism to absorb prior knowledge of medical entity labels,achieving local feature enhancement. Then in global feature extraction, a lightweight recurrent convolutional neural network(LRCNN) is used to suppress parameter growth while preserving the original semantics of the text. The ablation and comparison experiments are conducted on three public medical text intent recognition datasets to validate the performance of the model. The results show that F1 score reaches 87.34%, 81.71%, and 77.74% on each dataset,respectively. The results show that the BRELS model can effectively identify and understand medical terminology, thereby effectively identifying users' intentions, which can improve the quality and efficiency of online medical services.

关 键 词：医疗问句分类 医疗实体标签语义 自适应融合机制 先验知识 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]