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针对中医医案诊断分类研究中上下文语义捕捉不足，难以有效捕捉长距离依赖信息以及分类精确度低等问题，提出了一种结合文本卷积神经网络(TextCNN)和门控循环单元(GRU)的混合模型。首先，利用Word2Vec模型对词向量进行训练，构建局部词向量库。其次，采用文本卷积神经网络对中医医案文本进行特征提取，以捕捉局部重要信息。最后，利用门控循环单元对提取的特征进行上下文信息建模，从而显著增强模型对长依赖关系的处理能力。实验结果表明，该模型在中医医案诊断文本分类任务中表现出色，预测精度达到85.01%，F1值为81.86%。
Abstract：Aiming at the problems of insufficient context semantic capture,difficulty in effectively capturing long-distance dependence information and low classification accuracy in the study of TCM medical records diagnosis and classification,a hybrid model combining Text Convolutional Neural Network (TextCNN) and Gated Recurrent Unit (GRU) is proposed.Firstly,the Word2Vec model is used to train the word vector and construct the local word vector library.Secondly,the TextCNN is used to extract the text features of TCM medical records to capture local important information.Finally,the GRU is used to model the context information of the extracted features,thereby significantly enhancing the model's ability to process long dependencies.The experimental results show that the model performs well in the text classification task of TCM medicals records diagnosis,the prediction accuracy reaches 85.01%,and the F1 value is 81.86%.
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基本信息:
DOI：10.19850/j.cnki.2096-4706.2025.06.008
中图分类号:R249;TP183;TP391.1
引用信息:
[1]邱雪峰,查青林,苗震等.基于卷积神经网络的中医医案诊断分类方法[J].现代信息科技,2025,9(06):39-45.DOI:10.19850/j.cnki.2096-4706.2025.06.008.
基金信息:
江西省科技厅重点研发计划项目(20171ACG70011);江西省科技厅重点研发计划项目(20203BBG72W008)