主 办:北 京 中 医 药 大 学
ISSN 1006-2157 CN 11-3574/R

JOURNAL OF BEIJING UNIVERSITY OF TRADITIONAL CHINESE MEDICINE ›› 2021, Vol. 44 ›› Issue (4): 358-365.doi: 10.3969/j.issn.1006-2157.2021.04.011

• Science & Technology • Previous Articles     Next Articles

Applied research of SANN model in the diagnosis of traditional Chinese patterns with six diseases data as examples*

Xue Zhe1, Zhao Zongyao1, Chen Jiaxu1#, Liu Yueyun1, Wang Xihong2, Xu Mengbai3, Dong Shuo1, Li Tongtong1, Wang Jun1   

  1. 1 School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China;
    2 Dongfang Hospital of Beijing University of traditional Chinese Medicine, Beijing 100029, China;
    3 Dongzhimen Hospital of Beijing University of traditional Chinese Medicine, Beijing 100029, China
  • Received:2020-10-04 Published:2021-04-29
  • Contact: Prof. Chen Jiaxu, 11 Beisanhuan Donglu, Chaoyang District, School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029. E-mail: chenjx@ bucm.edu.cn
  • Supported by:
    National Natural Science Youth Foundntion(No.81803972)

Abstract: Objective To study the applicability and strength of the statistical attention-based neural network (SANN) model in the diagnosis of TCM patterns, and to explore whether the generated feature contribution is aligned with TCM principles. Methods A total of 1,110 cases of hyperlipidemia, menopausal syndrome, coronary heart disease, chronic gastritis, chronic nephritis, urinary tract infection, and fatty liver recorded in the ancient and modern medical records cloud platform and the Chinese medicine Xinglinyuan database were selected. Diagnostic models were established through artificial neural network (ANN), random forest (RF), support vector machine (SVC), K-nearest neighbor (KNN), and statistical attention-based neural network model (SANN) respectively. Evaluation indicators include Macro-F1, Macro-Precision, Macro-Accuracy, and Macro-Recall. Results The statistical attention-based neural network model (SANN) in the 6 diseases has an average of Macro-F1 at 0.78, Macro-Precision at 0.79, Macro-Accuracy at 0.79, and Macro-Recall at 0.8, which were better than the other 4 benchmark models. The interpretability of its parameters and the support of derived features conformed to the principles of Chinese medicine. Conclusion The neural network model based on statistical attention (SANN) is applicable and advanced in undertaking tasks such as the intelligent diagnosis of TCM patterns, feature screening of TCM data, and the development of disease evaluation scales, thus providing an innovative methodological reference for related study.

Key words: Statistical Attention-based Neural Network(SANN), diagnosis of patterns, feature screening

CLC Number: 

  • R241.9