Abstract

Info

Title:: Disease text classification model based on two-channel neural network

Author(s):: YUAN Ye; LIAO Wei; School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Keywords:: Keywords: disease text classification self-attention convolutional neural network bidirectional long short-term memory network

Abstract:: Abstract: The accurate text classification for diseases plays an important role in promoting the development of medical informatization. A method for studying disease text classification based on a two-channel neural network model is proposed. In the model, convolutional neural network and bidirectional long short-term memory network are used to learn the local features and temporal features of disease symptom text input by patients. In addition, self-attention mechanism is introduced to bidirectional long short-term memory network for distinguishing the contribution value of the feature to classification prediction, which enhances the learning ability and interpretability of the model. The model combining two kinds of features for enabling the features extracted from the two channels to jointly determine classification results, and finally softmax classifier is used to obtain the final classification results. Experimental results show that the accuracy, recall rate and F1 value of the proposed model are 90.61%, 90.48% and 90.51%, respectively, which were higher than those of other classification models.