Computer Science ›› 2022, Vol. 49 ›› Issue (1): 153-158.doi: 10.11896/jsjkx.201100125

• Database & Big Data & Data Science • Previous Articles     Next Articles

Aided Disease Diagnosis Method for EMR Semantic Analysis

FAN Hong-jie1, LI Xue-dong2, YE Song-tao3   

  1. 1 The Department of Science and Technology Teaching,China University of Political Science and Law,Beijing 102249,China
    2 School of Software and Microelectronics,Peking University,Beijing 102600,China
    3 School of Computer Science,Xiangtan University,Xiangtan,Hunan 411105,China
  • Received:2020-11-17 Revised:2021-04-16 Online:2022-01-15 Published:2022-01-18
  • About author:FAN Hong-jie,born in 1984,Ph.D,lecturer.His main research interests include data exchange and knowledge graphs.
    YE Song-tao,born in 1983,Ph.D,asso-ciate professor.His main research in-terests include truth discovery,data analysis and data mining.
  • Supported by:
    National Natural Science Foundation of China(61802327) and Natural Science Foundation of Hunan Province (2018JJ3511).

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.

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

CLC Number: 

  • TP391.4
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