Research on Diagnosis and Prescription System of Coronary Heart Disease with Syndrome Elements Based on Improved Transformer Algorithm
10.13422/j.cnki.syfjx.20221347
- VernacularTitle:基于改良Transformer算法的冠心病证候要素诊断处方模型分析
- Author:
Hongzheng LI
1
;
Jie WANG
1
;
Yuchen GUO
2
;
Zhenpeng ZHANG
1
;
Jiannan LI
3
;
Qianyi LI
4
;
Yan DONG
1
;
Qiang DU
3
Author Information
1. Guang′anmen Hospital，China Academy of Chinese Medical Sciences，Beijing 100053，China
2. School of Information Science and Technology，Tsinghua University，Beijing 100084，China
3. Shenzhen International Graduate School，Tsinghua University，Shenzhen 518055，China
4. Imperial College London，London SW7 2AZ，United Kingdom
- Publication Type:Journal Article
- Keywords:
coronary heart disease;
traditional Chinese medicine （TCM）;
syndrome elements;
machine learning;
attention mechanism;
bidirectional encoder representations from Transformers （BERT）;
artificial intelligence （AI）
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2023;29(1):148-154
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo construct a traditional Chinese medicine （TCM） syndrome diagnosis and prescription model for coronary heart disease with the improved Transformer algorithm. MethodTaking the syndrome elements of coronary heart disease as key links， the model was constructed based on the clinical diagnosis and treatment principle of "symptoms-syndrome elements-syndrome-treatment method-prescription-medicine （dose）". The basic logic of improved Transformer algorithm was constructed with multi-head attention mechanism， compound term vector and dropout， in order to form the model with functions of TCM syndrome elements judgment， syndrome diagnosis， prescription recommendation. After the model was constructed， it was trained by 8 030 cases. And 100 cases with TCM prescriptions were randomly selected for testing， and the model output prescriptions were compared with those of clinicians for qualitative evaluation of the model. ResultThe improved Transformer with multi-head attention improved the accuracy of the model. The model was consistent with clinicians in the judgment of main syndromes and the selection of prescriptions. Whereas there was a certain room for improvement in the addition and subtraction of medicines. ConclusionThe improved Transformer model can improve the accuracy and stability of output， which is an embodiment of the intelligent development of TCM.
