Abstract: The traditional medical text data classification methods ignore the context of the text. Each word is independent of each other and cannot represent semantic information. The text description and classification effect are poor, and feature engineering requires manual intervention, so the generalization ability is not strong. Aiming at the problems of low efficiency and low accuracy of medical text data classification, this paper proposes a medical text classification model CMNN based on bidirectional encoder representations from Transformer(BERT), convolutional neural network (CNN) and Bi- directional long and short-term memory (BiLSTM) neural network. The model uses BERT to train word vectors and combines CNN and BiLSTM to capture local latent features and contextual information. Finally, the proposed model is compared with the traditional deep learning models TextCNN and TextRNN in terms of accuracy, precision, recall and F1 score. The experimental results show that the CMNN model outperforms other models on all evaluation metrics, and the accuracy is improved by 1.69%~5.91%.

Key words: natural language processing, medical text classification, BERT, CNN, BiLSTM