Abstract: Aiming at solving the problem of auxiliary disease diagnosis for electronic medical record,the word vector and text discrimination method are applied to the semantic text analysis task.Concretely,the pre-training language model is used as the semantic representation of characters,so as to accurately express the text features.After extracting N-ary features from convolutional neural network,the capsule unit is used to cluster the features,so as to better capture the high-level semantic text features and reduce the demand for data.It is found that the combination model based on ERNIE+CNN+Capsule achieves high accuracy on the real EMR.In addition,inspired by the image style transfer,a style conversion model from EMR text to disease self-report text is trained.Based on the style conversion model,non-parallel data are used to add confrontation ideas and confusion evaluation indexes,which can effectively alleviate the problem of inconsistent distribution of training data and test data.Finally,compared with ALBERT_tiny,BERT and other models,the proposed model gets 86.89% F1 value in the EMR,which is improved by1.36%~3.68%,and 94.95% F1 value in the generalization.Experiments show that the proposed model can effectively adapt to the auxiliary disease diagnosis on the premise of ensuring high accuracy.

Key words: Auxiliary diagnosis, Capsule network, Deep neural networks, Electronic medical record, Semantic analysis