Aided Disease Diagnosis Method for EMR Semantic Analysis

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 ALBERTtiny,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.

Keywords

• electronic medical record|semantic analysis|auxiliary diagnosis|deep neural networks|capsule network