基于医疗知识驱动的中文疾病文本分类模型

Chinese disease text classification model driven by medical knowledge

本文提出一种基于医疗知识驱动的中文疾病文本分类模型。首先,通过引入外部医疗知识图谱中的结构化知识,得到知识增强的疾病文本向量表示;其次,使用双向长短期记忆网络和卷积神经网络分别提取疾病文本的全局语义特征和局部语义特征,同时,联合注意力机制提高模型对有效特征信息提取的效率;最后,将提取到的特征进行拼接融合,并利用分类器输出分类结果。在中文疾病文本数据集上的实验结果表明,所提模型分类的精确率、召回率和精确率和召回率的调和均值F1值分别可达95.21%、95.64%和95.42%,与其他模型相比具有更优的分类效果。

This study proposes a Chinese disease text classification model that integrates knowledge graph.Firstly,by introducing structured knowledge from external medical knowledge graph,a knowledge enhanced disease text vector representation is obtained;Secondly,the global semantic features and local semantic features of the disease text are extracted by using bidirectional long shortterm memory network and convolutional neural network respectively.At the same time,the joint attention mechanism improves the efficiency of the model in extracting effective features information;Finally,the extracted features are concatenated and fused,and a classifier is used to output the classification result.The experimental results on the Chinese disease text dataset show that the proposed model has a classification accuracy,recall,and the harmonic mean value F1 of 95.21%,95.64%,and 95.42%,respectively,which shows better classification performance compared to other models.