Research on text classification of traditional Chinese medicine medical records with ERNIE model

Text classification is one of the basic tasks of text mining. In the medical field, a lot of research has been conducted on text classification of English clinical notes of modern medicine. This paper studies the classification of common diseases in than that of BERT_CNN model, indicating that the Traditional Chinese Medicine(TCM) clinical records based on actual data. We explored many of the most advanced deep learning models and found recent advances in Enhanced Representation through Knowledge Integration(ERNIE) models can achieve better results than other deep learning models and other state-of-the-art methods. First, ERNIE model is used to obtain more fully semantic distributed text representation. Then, the deep convolutional neural network is introduced to further extract the encoding features of context to obtain deeper text feature representation. Finally, a classifier (Soft Maximum, Softmax) is used to classify common diseases. Based on a large number of medical case data sets obtained from a hospital, the comparison experiment of multiple groups shows that the F1 value is 3.2% higher than BERT_CNN model, and the accuracy rate is 4.4% higher than BERT_CNN model, and the accuracy rate is 0.7539. The results show that the TCM text classification method based on ERNIE model can effectively improve the performance of Chinese text classification.